# Advancements in Arc Fault Detection for Electrical Distribution Systems: A
Comprehensive Review from Artificial Intelligence Perspective

Kriti Thakur, Divyanshi Dwivedi, K. Victor Sam Moses Babu, Alivelu Manga
Parimi, Pradeep Kumar Yemula, Pratyush Chakraborty, Mayukha Pal (Corresponding
author: Mayukha Pal)Ms. Kriti Thakur is a Data Science Research Intern at ABB
Ability Innovation Center, Hyderabad 500084, India, and also a Ph.D. Research
Scholar at the Department of Electrical and Electronics Engineering, BITS
Pilani Hyderabad Campus, Hyderabad 500078, IN.Mrs. Divyanshi Dwivedi is a Data
Science Research Intern at ABB Ability Innovation Center, Hyderabad 500084,
India, and also a Research Scholar at the Department of Electrical
Engineering, Indian Institute of Technology, Hyderabad 502205, IN.Mr. K.
Victor Sam Moses Babu is a Data Science Research Intern at ABB Ability
Innovation Center, Hyderabad 500084, India and also a Ph.D. Research Scholar
at the Department of Electrical and Electronics Engineering, BITS Pilani
Hyderabad Campus, Hyderabad 500078, IN.Dr. Alivelu Manga Parimi is a Professor
in the Department of Electrical and Electronics Engineering, BITS Pilani
Hyderabad Campus, Hyderabad 500078, IN.Dr. Pradeep Kumar Yemula is an Assoc.
Professor with the Department of Electrical Engineering, Indian Institute of
Technology, Hyderabad 502205, IN.Dr. Pratyush Chakraborty is an Asst.
Professor in the Department of Electrical and Electronics Engineering, BITS
Pilani Hyderabad Campus, Hyderabad 500078, IN.Dr. Mayukha Pal is with ABB
Ability Innovation Center, Hyderabad-500084, IN, working as Global R&D Leader
– Cloud & Analytics (e-mail: mayukha.pal@in.abb.com).

###### Abstract

This comprehensive review paper provides a thorough examination of current
advancements and research in the field of arc fault detection for electrical
distribution systems. The increasing demand for electricity, coupled with the
increasing utilization of renewable energy sources, has necessitated vigilance
in safeguarding electrical distribution systems against arc faults. Such
faults could lead to catastrophic accidents, including fires, equipment
damage, loss of human life, and other critical issues. To mitigate these
risks, this review article focuses on the identification and early detection
of arc faults, with a particular emphasis on the vital role of artificial
intelligence (AI) in the detection and prediction of arc faults. The paper
explores a wide range of methodologies for arc fault detection and highlights
the superior performance of AI-based methods in accurately identifying arc
faults when compared to other approaches. A thorough evaluation of existing
methodologies is conducted by categorizing them into distinct groups, which
provides a structured framework for understanding the current state of arc
fault detection techniques. This categorization serves as a foundation for
identifying the existing constraints and future research avenues in the domain
of arc fault detection for electrical distribution systems. This review paper
provides the state of the art in arc fault detection, aiming to enhance safety
and reliability in electrical distribution systems and guide future research
efforts.

###### Index Terms:

Arc fault detection, Electrical distribution, Artificial intelligence, Machine
learning, Neural networks.

##  I Introduction

Electrical energy plays a crucial role in the world’s economy, serving as a
key factor in the quality of living within a society. It empowers the movement
of both goods and people, fuels manufacturing operations, facilitates diverse
communication methods, and ensures uninterrupted data storage – all of which
are vital components of the modern economy. Additionally, it plays an
indispensable role in advancing medical diagnostics, optimizing hospital
management, and supporting critical functions in airports and other essential
sectors, ultimately enhancing human capabilities. In the current era, there is
a growing worldwide consumer demand for power utilities to provide power
supply characterized by improved reliability and superior quality. The
complexity of the electrical distribution system is steadily increasing due to
the greater integration of distributed energy resources [1, 2]. Thus,
electricity has transitioned from being a premium commodity to a more
accessible and commonplace resource. It has become an important resource with
significant economic and security significance. However, power outages and
blackouts represent significant disruptions in the continuous functioning of
essential services, posing critical interruptions to daily life and routine
activities.

Fires pose a significant risk to the safety and well-being of human beings.
Electrical fires are a commonly encountered category of fires, comprising an
average of 30.2% of all fire incidents and surpassing 50% of all incidents
classified as big fires [3]. By statistical data obtained from fire
administrations, it has been observed that electrical fires commonly arise due
to several factors such as arc fault, over current, short circuit, and leakage
[3, 4]. According to the findings of the United States Fire Administration
(USFA), the sources of heat in electrical fires occurring in residential
buildings, it was determined that arc fault was responsible for 82% of these
incidents [5]. Thus, arc faults are widely recognized as a significant factor
contributing to electrical fires [5, 6]. According to data provided by the
USFA, an approximate total of 372,900 incidents of residential fires in
buildings occurred annually in the United States between 2011 and 2013. These
fires resulted in approximately 13,125 injuries, 2530 accidental deaths, and
property damage amounting to 7 billion dollars [5]. In the United States, an
annual average of about 1700 individuals suffer fatalities or sustain injuries
as a result of electrical fires [7]. In the year 2019, the Korean government
released a report discussing the factors contributing to 23 fire incidents in
energy storage systems (ESSs). The insufficiency of electrical protection
mechanisms in these systems, particularly in terms of safeguarding against
Direct Current (DC) arc faults, is highlighted [8]. The key contributions of
the work are as follows:

  * •

A thorough review and analysis of the latest advancements in the field of arc
fault detection for electrical distribution systems. This includes a
comprehensive overview of emerging technologies and methodologies.

  * •

The importance of early detection and identification of arc faults to prevent
potential catastrophic accidents, such as fires, equipment damage, and loss of
life. It emphasizes the proactive role of arc fault detection in mitigating
risks.

  * •

The role of AI in the detection of arc faults. It provides the superior
performance of AI-based methods in accurately identifying arc faults,
providing a detailed examination of how AI contributes to enhanced detection
capabilities.

  * •

Identification of existing gaps in arc fault detection technologies and
provides a basis for future research directions.

The review is organized as follows: Section I provides the background of arc
faults and their effect on the surroundings and why we have to detect arc
faults. Section II provides an overview of an arc fault. Section III presents
the overview of arc fault detection methods. Section IV provides the
challenges of the traditional methods and opportunities of AI-based methods
and the comparison from the literature. Finally, in Section V, conclusions on
the comprehensive exploration are provided with a discussion on the future
scope for arc fault detection.

##  II Arc Fault Overview

As per the guidelines outlined in the UL Standard UL1699 [9], an arc fault is
characterized as the sustained release of electrical energy in the form of
light through an insulating medium, which is frequently followed by the
partial vaporization of the electrodes. The occurrence of this phenomenon
could be attributed to several factors, including the deterioration of the
insulating medium inside the electrical circuit, the weakening of electrical
connections, the presence of humidity in the surrounding air, and a decrease
in the insulation strength of the conducting cable. The elevated temperature
resulting from the occurrence of an arc fault discharge phenomenon possesses a
strong tendency to initiate combustion in the adjacent combustible materials,
hence constituting a notable contributing factor to the incidence of
electrical fires. The increasing reliance of societies on energy and the
growing interconnectedness of their infrastructures necessitate the
prioritization of safe and reliable functioning of distribution grids. This
entails the development of fault detection systems to limit the consequences
of power outages/electrical fires. Therefore, arc fault detection is of the
utmost importance.

The occurrence of an electric arc phenomenon arises when a short circuit
happens, resulting in the flow of a substantial amount of current. As the
contacts of the circuit breaker separate, the contact area diminishes, leading
to an increase in current density and a subsequent elevation in temperature.
This rise in temperature causes the ionization of the surrounding air. The
ionized air acts as a conductor between the contacts, and an arc is struck
between the contacts. Common forms of damage often observed in such scenarios
encompass the occurrence of excessive heat in copper wiring, the liquefaction
of aluminum rods, and the release of hazardous gases. The occurrence of
overheating could lead to hazardous elevations in pressure, hence resulting in
the potential explosion of switchgear. An illustrative image of a practical
arc fault persisting within the combiner box is depicted in Fig. 1.

Figure 1: An illustrative image of a practical arc fault persisting within the
combiner box [10]. Figure 2: Various types of arc faults.

Arc fault could be classified into three types: series arc fault, parallel arc
fault, and ground arc fault, depicted in Fig. 2 [11]. A series arc fault
pertains to conductor disconnection in power transmission lines, while a
parallel arc fault occurs due to insulation breakdown between two or more
parallel lines caused by external forces or heat. A ground arc fault occurs
when an unintentional electrical connection is established between an
electrical conductor, such as a wire, and the earth’s surface. This situation
may result in an abnormal current flow, which has the ability to cause harm to
equipment, spark fires, or pose dangers to electrical systems. Distinguishing
between an arc fault and a normal signal is vital in the context of arc fault
detection. It is imperative to find the presence of elevated current and
voltage levels, as well as high-frequency fluctuations, together with the
accompanying manifestations of sound, vibration, light, and electromagnetic
energy that are indicative of fault arcs [12, 13]. The energy generated during
an arc fault, according to Joule’s law, is:

$\Delta E=R\,I^{2}\Delta t$ (1)

where $R$ is the overall current path’s resistance which includes the arc
impedance, $I$ represents the fault current, and $\Delta t$ represents the
period when the fault exists.

Notably, series arc faults are the most commonly occurring type [14, 15, 16].
Series arc faults are considered as an additional resistance inside the
electrical system, resulting in a reduction of the arc fault current. As a
result, traditional protective mechanisms are unable to be activated. If
series arc faults are not promptly discovered and mitigated, they have the
potential to impact the associated circuits within the system, cause damage to
the electricity supply channels and system controllers, and potentially result
in blasts [17, 18]. Arc faults in electrical systems may cause substantial
risks, giving rise to a range of safety considerations and severe harm. A
comprehensive awareness of these potential dangers is important to effectively
build, uphold, and manage electrical systems with utmost safety. The most
significant risks associated with arc faults are illustrated in Fig. 3.

Figure 3: The most significant risks associated with arc faults.

###  II-A Arc Fault Characteristics

The electrical behavior of arc faults is inherently complicated, chaotic, and
unpredictable, and it is easily understood and represented using an equivalent
circuit model [19, 20, 21]. The circuit depicted in Fig. 4 exhibits a gap
inside the conduction route, which is connected in series with a line
inductance denoted as $L$. Additionally, the circuit has a load that is
powered by a (DC) voltage source represented by $V$. The distance $x$ denotes
the gap separation between an electrode that remains fixed and the other
electrodes that move away at a velocity $u$, which varies with time. Upon the
first separation at the gap, it is believed that an arc is formed,
characterized by an arc voltage $v_{arc}$ and an arc current $i_{f}$.

The primary parameters utilized to depict the electrical properties of an arc
and assess their influence on the power system in which they manifest are the
arc voltage, current, and resistance.

Figure 4:  The equivalent electrical model representing a DC series arc fault.

  * •

Arc Voltage: Arc voltage refers to the electrical potential difference that
develops between the boundaries of the gap. The initiation of an arc
necessitates a minimum voltage, which subsequently escalates until the arc is
terminated. The voltage stability and sustainability of an arc are dependent
upon the length of the gap. If the gap length is within an appropriate range,
the voltage will stay in stable condition, hence facilitating a prolonged arc.
Conversely, if the gap length exceeds a certain threshold, the arc will
undergo self-extinguishment.

  * •

Arc Current: The arc current refers to the electric current that passes
through the gap. The behavior of arc current is inversely related to arc
voltage. Upon initiation of the arc, the current experiences an abrupt fall.
As the arc voltage increases, the current continues to decline until the arc
is sustained. Conversely, if the arc has been quenched, the current will reach
zero.

  * •

Arc Resistance: The behavior of a series arc exhibits resistive
characteristics, characterized by a highly nonlinear arc resistance due to the
unpredictable nature of the arc phenomena. The arc resistance could be
determined by measuring the arc voltage and current at various time intervals.
This calculation is commonly performed by averaging the arc resistance across
the stable burning duration.

###  II-B Arcing Fault Model

In the context of a distribution system, it is possible to represent a branch
that experiences an arcing fault by employing a model wherein an arc is
connected in series with a constant resistance $R_{0}$ [22]. The general
arcing fault model in the distribution system is shown in Fig. 5, where
$R_{arc}(t)$ is the time-varying arc resistance. Let
$g_{arc}(t)=\frac{1}{R_{arc}(t)}$ be the arc conductance, which is also time-
varying. Thus, if $i_{f}(t)$ is the arc current, the voltage across the arc
could be expressed as:

$v_{f}(t)=i_{f}(t)\cdot R_{0}+\dfrac{i_{f}(t)}{g_{arc}(t)}$ (2)

The utilization of an arc model (AM) is necessary in order to accurately
represent the function $g_{arc}(t)$ for the purpose of modeling the detection
challenge. Extensive study has been carried out on AMs over the course of a
couple of decades, resulting in the proposal of numerous AMs to address
various motives, such as the analysis of circuit-breaker arcs [23], arc energy
calculation [24], and arcing fault analysis [25, 26].

The AM that connects $g_{arc}(t)$ and the current of the arc branch $i_{f}(t)$
is as follows:

$\dfrac{dg_{arc}(t)}{dt}=\dfrac{1}{\tau}\left(\dfrac{|i_{f}(t)|}{u_{0}+r_{0}\,|i_{f}(t)|}-g_{arc}(t)\right)$
(3)

where $u_{0}$ is the characteristic arc voltage, $r_{0}$ is the characteristic
arc resistance, and $\tau$ is the arc time constant.

Figure 5: The distribution system’s general arcing fault structure [27].

Various types of loads have a significant impact on arc fault currents. The
fault currents generated by specific household appliances exhibit minimal
variations, and the characteristics of arc faults are typically obscured by
load currents and ambient noise. Finding a fault feature that consistently
performs for all types of loads poses a significant challenge. In addition, it
should be noted that the home the electrical norm for single-phase alternating
current (AC) in the United States is 120 V (60 Hz), although in other
countries such as China, Germany, Switzerland, and Korea, it ranges from 220 V
to 240 V (50 Hz to 60 Hz). Arc faults are more likely to result in electrical
fires when higher voltages are present[28, 29, 30, 31]. Given a case study in
[28], the probability of fire initiation resulting from arc faults exhibits an
escalation from 3.5% for a voltage of 120 V to 83% for a voltage of 240 V. It
is worth noting that the nominal current levels for both scenarios remain
constant at 15 A. In contrast to the voltage of 120 V, the elevated voltages
ranging from 220 to 240 V have a greater propensity to induce gap breakdown
and generate a bigger number of arcs [29, 30]. Arc currents often exhibit a
continuous pattern in systems operating at higher voltages, however in 120 V
systems, they may occasionally display an intermittent behavior [28, 29].
Consequently, it could be observed that systems operating at higher voltage
levels have the ability to create greater arc energy, thereby creating more
favorable circumstances for the initiation of electrical fires [29, 30].

###  II-C How to Prevent Arc Faults?

The existence of an arc fault signifies a severe breakdown in the power
system. An arc flash incident has the potential to inflict considerable harm
on individuals engaged in operations, lead to substantial financial losses
stemming from equipment damage, and trigger extended periods of system
downtime. The extinction of this arc is achieved through the process of
cooling and elongation of the arc. During the procedure of interrupting a
circuit, the arc that is formed serves the purpose of dissipating the energy
that is stored in the inductance of the circuit. Following the successful
suppression of the arc, the dielectric strength commences its healing process,
while the circuit breaker successfully separates the fault, under the
assumption that no re-striking happens. The categorization of circuit breakers
is established based on the particular arc quenching medium utilized and the
cooling configuration utilized within the breaker. The following discussion
provides a concise analysis of the air arc chute circuit breaker, vacuum
interrupter, and SF6 circuit breaker as illustrative examples:

  1. i.

Vacuum circuit breakers employ a confined container filled with a vacuum as
the medium for extinguishing electrical arcs, owing to the high dielectric
strength exhibited by the vacuum. Vacuum circuit breakers are extensively
utilized in medium voltage AC distribution grids due to their numerous
advantages. These advantages include their self-contained nature and
compatibility with the environment, their high reliability and low maintenance
requirements, their non-flammability, their compact size and short stroke,
their low energy consumption for operation, and their extended service life.

  2. ii.

Sulfur hexafluoride (SF6) circuit breakers employ pressurized SF6 gas within a
confined container as the medium for extinguishing electrical arcs. Sulfur
hexafluoride is introduced in a controlled manner during the arc interruption
procedure to aid in the suppression of the electric arc. SF6 is frequently
employed in high-voltage AC networks [32].

  3. iii.

Molded-case circuit breakers (MCCBs) are essential components employed in
numerous distribution systems to safeguard against network disturbances [33].
During a fault event, a substantial surge in electric current flows through
the terminals of the MCCB, causing the contacts to separate due to the
activation of an electromagnetic mechanism. Upon initiation, a low-voltage
electric discharge is established between the contact points, resulting in the
formation of a high-temperature plasma with temperatures reaching several
thousand degrees Celsius[34]. The presence of plasma induces thermal
degradation in the insulating materials and erosion of the contacts, while
also emitting high UV radiation that could exacerbate the breakdown of the
insulating materials. The production of electrically conductive soot in a
polyester-based MCCB has a negative impact on the device’s long-term
functioning. Arc chutes are employed within the MCCB to elongate or divide the
arc and facilitate its cooling through thermal exchange. This aids in the
expeditious extinguishing of the arc, hence decreasing the thermal
deterioration of the insulating materials and lowering erosion of the
contacts. The Arc Vault protection system [35] represents a significant
advancement in arc fault prevention technology. It is capable of rapidly
interrupting an arcing fault within a timeframe of less than eight
milliseconds. This system effectively enhances the mitigation of arc flash
hazards, regardless of whether equipment doors are open or closed. The
implementation of this rapid arcing fault containment technique has the
potential to mitigate the extent of work-related injuries and minimize the
loss of productivity.

In contrast to the conventional circuit breaker, the Asea Brown Boveri (ABB)’s
solid-state circuit breaker employs power electronic components and
sophisticated software algorithms to replace the mechanical components found
in classic electromechanical circuit breakers. This substitution enables the
solid-state circuit breaker to swiftly regulate power and interrupt current
flow. The ABB circuit breaker concept originated by ABB’s Research and
Development unit situated in Bergamo, Italy. The circuit breaker employs the
Insulated Gate Commutated Thyristor (IGCT) semiconductor technology plan,
which incorporates an innovative incorporated predictive power management
software, protection algorithm, and advanced-level communication [36].

Figure 6: Product image of Arc Fault Protection System [37].

The REA system, a product by ABB [37], is an efficient and adaptable arc fault
protection system designed for air-insulated switchgear operating at low
voltage and medium voltage levels; the image is illustrated in Fig. 6. The REA
101 main module functions autonomously or in conjunction with other REA 101
modules and extension modules. The REA 103 extension module expands the arc
flash detection area by incorporating two sensor fibers arranged either in a
loop or radially. Meanwhile, the REA 105 extension module identifies arcs
using a loop-type or radial sensor fiber configuration and offers two high-
speed semiconductor outputs for circuit breaker tripping. Additionally, it
serves as an overcurrent information link between two REA 101 main modules.
Lastly, the REA 107 extension module is designed for arc detection, employing
eight lens sensors arranged radially.

The integration of a rapid and discerning arc fault protection system is an
inherent component of contemporary switchgear panels. Furthermore, it serves
as a prudent investment in safety and security for antiquated switchgear, as
it effectively safeguards human lives and mitigates the risk of asset
impairment.

The REA arc fault protection system employs two distinct types of sensors to
detect light. The first type is a non-shielded, bare-fiber sensor that is
capable of sensing light across its whole length. The second type consists of
light-collecting lens-type sensors, with normally one sensor installed in each
switchgear compartment [37].

The functionality of the REA arc fault protection system is predicated on the
identification of either the intense luminosity emitted by an electric arc in
isolation or the detection of luminosity in conjunction with concurrent phase
or neutral overcurrent. Upon the identification of an arc fault, the REA arc
fault protection mechanism promptly issues trip commands to all circuit
breakers responsible for supplying power to the area affected by the fault,
accomplishing this task within a time frame of less than 2.5 milliseconds. In
addition, the operational indications of the REA arc fault protection system
aid in the localization of problems by providing selective guidance to
maintenance personnel toward the specific fault zone that has been recognized
by the arc fault protection system.

ABB provides active arc fault protection solutions that encompass both arc
fault detection and removal, hence mitigating the aforementioned dangers.
Collectively, these systems are engineered to identify an internal arc within
a time frame of 1.5 milliseconds and subsequently mitigate it within a
duration of less than 4 milliseconds. As a result, the enhancement of the
electrical system’s reliability and security may be greatly increased.

Figure 7: An electrical fire that arises as a consequence of an arc fault.

Arc Fault Circuit Interrupters (AFCIs) is an instrument that interrupts a
circuit if it recognizes a dangerous electrical arc by recognizing the
patterns of electrical arcs. Individual circuits are protected by these, which
are generally installed in household and commercial electrical panels. AFCIs
help in protecting both parallel and series arcs in connected loads and the
wiring [38]. AFCIs rely on current as a measurement parameter due to its ease
of measurement and the absence of a need to modify the circuit or wire
connections. Time-domain characteristics are retrieved to detect arcs. Arc
faults refer to unintentional electrical arcs that have the potential to
ignite combustible objects within a residential structure. The AFCI is
characterized by its compact size, ease of installation, and its ability to
effectively handle shared and mixed neutrals. The AFCI complies with the
National Electrical Code of 2020 [39].

###  II-D Why Fault Detection is Important?

The timely identification of faults, whether in equipment or the intermediate
products of manufacturing processes, carries substantial importance. This is
vital to uphold the standards of the end product, prevent the occurrence of
abnormal operating circumstances, mitigate costly repairs, and avert potential
shutdowns in the production process. Utilizing a typical method for network
operation, characterized by an operating period ranging from 80 to 100
milliseconds, leads to the occurrence of cable fire as well as the melting of
copper and steel components [40]. The increasing intricacy of industrial
systems and the expanding volume of accessible data have stimulated the
advancement of intelligent systems for automated fault prediction and
detection. These systems mostly rely on industry 4.0 technologies, with a
special emphasis on deep learning techniques. The presence of a substantial
and uninterrupted stream of data, along with the diverse nature and
fluctuating characteristics of the data at hand, poses significant challenges
in employing traditional data analysis methods for decision-making purposes,
such as in the domain of fault risk assessment. Consequently, there has been
an increasing inclination in the development of data analysis systems that
utilize AI approaches [41].

The application of machine learning and supervised learning algorithms in
fault recognition, diagnosis, and failure prediction has experienced
significant advancements over the past few decades due to the adoption of
digital technologies and the availability of data. The aforementioned
technological improvements have facilitated the development of precise models
that support the formulation of effective and sustainable policies aligned
with the strategic goals of industries. Consequently, these advancements have
improved the decision-making process. Hence, it is imperative to precisely
identify series arc problems to maintain electrical safety. Nevertheless, the
detection of series arc faults is a major obstacle in actual engineering
applications due to their inherent properties, including high level of
unpredictability [42]. Fig. 7 depicts the sequential stages involved in the
occurrence of an electrical fire resulting from an arc fault. The insulating
layer of the compromised electrical wire undergoes carbonization, leading to
the following occurrence of random arcs. With time, the carbonized pathways
undergo further expansion, resulting in the formation of visible arcs. The
aforementioned arcs have the capacity to discharge an immense quantity of
energy and could attain temperatures as high as 13,000 K [43]. The presence of
combustible objects in close proximity facilitates the generation of a fire
with relative ease. The primary objective of fault diagnosis is to efficiently
identify the precise locations of faults. Once faults are detected, they must
be appropriately classified to facilitate fault localization. Technical
processes need advanced supervision and fault identification to enhance safety
measures and reliability [44].

Understanding the techniques employed for fault detection is of paramount
significance as it enables the timely identification of faults, hence
preventing potential harm to the system. The methodology for fault diagnosis
is presented in Fig. 8. There are two distinct approaches for fault detection:
the model-based method, which is the traditional approach, and the process
history-based method, which is recognized as an AI-based approach [45, 46]. In
order to employ model-based approaches, it is necessary to have an appropriate
mathematical model that accurately represents the process system. The
aforementioned definition, or pre-existing knowledge, is primarily rooted in
the fundamental principles of physics and can encompass both quantitative and
qualitative aspects. The traditional model-based method is carried out in two
different ways:

  1. 1.

Quantitative Methods: These methods use mathematical modeling of physical
processes. These methods, assisted by computational advances, were developed
to handle increasingly complex systems and use analytical redundancy by
deriving precise mathematical descriptions.

  2. 2.

Qualitative Methods: These fault diagnosis methodologies encompass model-based
or knowledge-based approaches that rely on heuristic symptoms rather than
analytical descriptions to ascertain the present condition of the system.

In the context of process history-based techniques, the approach relies on a
substantial collection of experimental input/output data and endeavors to
establish a functional relationship solely based on these characteristics.

  1. 1.

Statistical Methods: Linear regression, multiple regression, polynomial
regression, principal component analysis, partial least squares, and logistic
regression.

  2. 2.

Non-Statistical Methods: Artificial neural networks (ANNs) and various other
pattern recognition techniques.

Figure 8: Various fault detection techniques.

In the realm of arc fault detection, various mathematical models of arcs have
been documented in the literature, and this discussion will outline some of
their fundamental aspects. Mathematical models are used to depict the dynamic
variations in the series arc using mathematical equations. These models
encompass the Cassie, Mayr, Habedank, and several other enhanced models [47,
48] and are known as physics-based AMs. The Cassie and Mayr AMs are highly
renowned and extensively employed AMs that were established based on the
notion of energy conversion. The Cassie AM could be mathematically represented
as follows:

$\dfrac{1}{g}\dfrac{dg}{dt}=\dfrac{1}{\tau}\bigg{(}\dfrac{u_{arc}\,i_{arc}}{U^{2}_{c}}-1\bigg{)}\\\
$ (4)

$g=\dfrac{i_{arc}}{u_{arc}}$ (5)

The variables in the equation are as follows: $g$ denoted as represents arc
conductance. $u_{arc}$ denoted as represents arc voltage. $i_{arc}$ denoted as
represents arc current. $U_{c}$ denoted as represents constant arc voltage.
$\tau$ denoted as represents the arc time constant. In this particular model,
Cassie assumed that the power loss was due to forced convection. This
assumption implies a straight proportionality between the area of the arc
cross-section and the arc current. Therefore, Cassie’s arc model is more
suited for simulating events that include substantial amounts of arc current.

In contrast, the Mayr AM is more appropriate for low current arcs due to the
assumption made by the Mayr that power loss is primarily attributed to thermal
conduction and remains constant [49]. The equation is illustrated as follows:

$\dfrac{1}{g}\dfrac{dg}{dt}=\dfrac{1}{\tau}\bigg{(}\dfrac{i_{arc}^{2}}{P_{loss}}\bigg{)}$
(6)

The variables in the equation are as follows: $P_{loss}$ denoted as power loss
The value of the unknown constant in equation (6) could be found by analyzing
and interpreting experimental data [50].

AMs of Cassie and Mayr are connected in series and make the Habedank model and
it is applicable to both large and small currents; the model includes numerous
parameters that are challenging to simulate. The equation of the Habedank
model is illustrated as follows:

$\dfrac{1}{g_{c}}\dfrac{dg_{c}}{dt}=\dfrac{1}{\tau_{c}}\bigg{[}\bigg{(}\dfrac{u_{arc}\,g}{U_{c}\,g_{c}}\bigg{)}^{2}-1\bigg{]}$
(7)

$\dfrac{1}{g_{m}}\dfrac{dg_{m}}{dt}=\dfrac{1}{\tau_{m}}\bigg{(}\dfrac{u_{arc}^{2}\,g^{2}}{P_{loss}\,g_{m}}-1\bigg{)}$
(8)

$\frac{1}{g}=\frac{1}{g_{c}}+\frac{1}{g_{m}}$ (9)

The variables in the equation are as follows: $g_{c}$ denoted as represents
the arc conductance of the Cassie model, $g_{m}$ denoted as represents the arc
conductance of the Mayr model, $\tau_{c}$ denoted as represents the arc time
constant of Cassie model, $\tau_{m}$ denoted as represents the arc time
constant of Mayr model.

Due to their arbitrary and aperiodic characteristics, mathematical models are
unable to adequately characterize the current waveforms resulting from series
arc faults in electrical systems under various conditions, as estimated by
these models. Hence, the methodologies grounded in mathematical models
predominantly remain within the boundaries of simulation.

The study of process history involves the examination and analysis of past
events and developments, with a particular focus on the processes and
mechanisms that these models are well-suited for situations in which training
data are abundant or where the cost of creating or collecting such data is
low. The mapping functions have been comprehensively studied and considerably
refined. Extensive study has been undertaken in the aforementioned area of
pattern identification algorithms, specifically in the domain of machine
learning. The aforementioned approaches exhibit significant resilience to
system noise in dynamic scenarios. Various approaches for detecting electrical
arcs are depicted in Fig. 9.

Figure 9: Various approaches for detecting electrical arcs.

##  III An outline of arc fault detection approaches

The present study classifies arc fault detection systems into two primary
categories: conventional approaches and methods based on AI. The predominant
approaches for detecting arc faults involve the study of the time domain,
frequency domain, time-frequency domain, or the observation of physical
phenomena such as sound, light, or electromagnetic radiation. The
methodologies employed in this study involve the examination of electrical
signals generated by the occurrence of an arc fault, intending to extract and
utilize the pertinent information embedded within these signals. The
aforementioned data could be employed to determine the presence of an arc
fault. In contrast, AI-based approaches employ machine learning algorithms to
analyze power system data to detect arc faults. The aforementioned
technologies have demonstrated notable levels of accuracy and efficiency in
the identification of arc defects, presenting a potentially advantageous
substitute for traditional approaches. Fig. 10 illustrates an outline of
various techniques employed for arc fault detection.

Figure 10: Outline of arc fault detection methods.

###  III-A Traditional Arc Fault Detection Approaches

Multiple approaches use different ideas to identify arc problems, including
analyzing electrical current and voltage waveforms or examining arc physical
characteristics. Arc fault detection techniques that utilize an electrical
quantity could be categorized into two different categories: techniques that
rely on current characteristics and techniques that rely on voltage
characteristics [51]. The foundations of conventional arc fault detection
techniques are the electromagnetic and thermal fingerprints of electrical
arcs. These techniques are frequently employed to locate hazardous arc faults
in electrical systems, which helps to avoid fires and property damage. Typical
conventional techniques for arc fault detection include:

####  III-A1 Time Domain-Based Arc Fault Identification Methods

Several detection algorithms have been developed that utilize time-domain
properties of current signals, such as shoulder length, change rate, and
signal energy [52, 53]. The chosen time-domain properties, however, are only
appropriate for a few particular kinds of loads. In [54], authors introduced
the utilization of time domain analysis as a means to identify the happening
of arc faults. This technique involves the examination of current and voltage
data. Voltage signals could be significantly affected by drops or fluctuations
in the supply voltage, and the characteristics of voltage are also susceptible
to variations in circuit types. The above-discussed approaches provide rapid
detection rates through the utilization of uncomplicated circuits and
algorithms. However, they are vulnerable to interference caused by switching
noise and fluctuations in load. Several time-domain techniques are commonly
employed to identify series arc faults, such as the finite difference method
or kurtosis analysis [55]. Voltage signals could be significantly affected by
drops or fluctuations in the supply voltage, and the characteristics of
voltage are also susceptible to variations in circuit types.

TABLE I: Conventional approaches used for arc fault detection.

Approach |  Method |  References
---|---|---
Time Domain |  Kurtosis Analysis, |
Finite Difference Method. |  [52, 53, 54, 55] |
Frequency Domain |  Discrete Wavelet Transform, |
Fast Fourier Transform, Discrete Fourier Transformation, and |  |
Chirp Zeta Transformation. |  [56, 57, 58, 59, 60, 61, 62, 63, 64, 65] |
Time-Frequency Domain |  Discrete Wavelet Transform, |
Empirical Mode Decomposition, |  |
Dynamic Temporal Warping, and |  |
Hilbert-Huang Transform |  [66, 67, 68, 69, 70, 71, 72, 73] |

####  III-A2 Frequency Domain-Based Arc Fault Identification Methods

In [56], authors employed the tunnel magnetoresistance (TMR) sensor to
quantify the alteration in the magnetic field caused by the arc. They further
utilized this data to compute the power spectrum density, hence facilitating
the detection of the arc. The frequency domain identification of arc fault
conditions also used the wavelet packet transform (DPT) and discrete wavelet
transform (DWT). The work in [57] introduces an approach for the detection of
arc fault that combines signal collection and higher-order cumulant
identification. This approach leverages the electromagnetic coupling
phenomenon of high-frequency current. It involves analyzing the coupling
signal of the high-frequency current and applying the higher-order cumulant
technique when arcing occurs to determine the kurtosis. The study introduces a
unified kurtosis threshold value applicable to various scenarios. The proposed
approach involves decomposing the frequency response to enhance resolution
within the specified frequency range. However, it is crucial to note that this
method may amplify the noise signal resulting from the occurrence of an arc
fault condition [58]. In the study conducted by [59], a detection algorithm
was presented that utilizes the cumulative sum of spectrum parity values
within the frequency range of 1kHz to 15kHz. The Fourier transform is a well-
established technique utilized for the investigation of frequency domains and
is extensively employed in the diagnosis of DC arc faults [74, 75]. The
methods described in [61, 62, 63], utilize correlation coefficients to
identify variations in signal form caused by the existence of arc faults. The
authors in [64] utilize algebraic derivatives of line currents as a feature in
their study on arc fault detection. The utilization of wavelet packet
technology is employed in [65] to perform the decomposition, reconstruction,
and normalization of the current signal. The investigation of the spectral
attributes of arc faults involves the integration of information entropy and
short-time Fourier transformation.

In a study conducted by [60], a method was introduced for the progressive
breakdown of a signal over a period of time. This algorithm was found to be
approximately seven times more efficient than the Fast Fourier Transform (FFT)
and also more cost-effective in terms of implementation for arc fault
detection devices. The advent of 5th-generation technology for communication
and artificial intelligence has led to significant advancements.

The frequency-domain transformation techniques encompass the discrete Fourier
transformation and the chirp zeta transformation. The discrete Fourier
transformation is commonly employed because of its capacity to efficiently
capture the characteristics of the frequency domain. Nevertheless, the
performance of the technique may be influenced by the selected sample
frequency and observation window. The wavelet transform is extensively
employed for feature extraction and fault detection due to its capacity to
offer enhanced local properties of the signal.

####  III-A3 Time-Frequency Domain-Based Arc Fault Identification Methods

The investigation of novel techniques in the time-frequency domain for the
detection of arc faults in electrical systems not only improves accuracy and
effectiveness but also broadens its potential applications in various domains.
One such domain is biomedical signal processing [76], where the analysis of
dynamic signals is highly valuable, particularly in fields like
electroencephalography and electromyography.

The author in [66, 76] advocated the use of discrete wavelet transformation
for conducting hybrid analysis in the time-and-frequency domain. This study in
[67] presents a novel methodology that integrates DWT, empirical mode
decomposition (EMD), and dynamic temporal warping (DTW) techniques to address
the issue of detecting low-voltage AC series arc faults. The DWT leverages the
temporal and spectral features of the arc current data in order to identify
the happening of arc faults. The process of EMD involves breaking down the
intricate arc fault current signal into a limited number of intrinsic mode
signals (IMFs). Subsequently, the instantaneous amplitude of the IMF signal is
acquired by the utilization of the Hilbert-Huang transform (HHT). This
amplitude serves as a distinguishing characteristic for the identification of
arc faults. The outcomes of arc fault detections are contingent upon the
outcomes derived from DWT and EMD. In cases where two distinct procedures
yield disparate outcomes, the DTW method is employed as a supplementary step
to ascertain the presence of an arc fault. This involves utilizing similarity
measures between normal and arc fault current waveforms. This study in [68]
presents a novel approach, namely the hybrid time and frequency analysis and
fully connected neural network (HTFNN), for the identification of series AC
arc faults. The HTFNN method employs a classification approach to identify the
state of samples by categorizing them into three main groups: resistive (Re),
capacitive-inductive (CI), and switching (Sw). This categorization is based on
the analysis of the fundamental frequency components of the series current.
For fine class recognition and state identification, a fully connected neural
network (NN) is utilized in each category. This NN is equipped with customized
time and frequency indicators as input. The overall accuracies for identifying
normal and arcing states in the Re, CI, and Sw categories are 99.64%, 100%,
and 98.45%, respectively.

Figure 11: The architecture of DNN employed by [77] is utilized for the
purpose of identifying and locating arc faults.

These approaches enhance the precision of detection; yet, they are unable to
attain feature threshold adaption. The extraction of time-frequency domain
aspects of the arc current is commonly performed by researchers through
calculation. Subsequently, these features are classified using a
classifier[69]. Despite the demonstrated improvements in performance, these
methods still need the human extraction of features and their subsequent input
into neural networks. The authors of [70] conducted a comparative analysis of
arc detection approaches utilizing synthetic waveforms, specifically focusing
on the performance of FFT and DWT. Their findings suggest that DWT generates
detection features that are more readily identifiable. In [71, 72, 73] use the
time-domain and frequency-domain information to find errors. They are suited
for a limited number of load types, and the detection accuracy would decline
as the number of load types increased. Table III-A1 presents a comparative
analysis derived from previous research articles on conventional approaches
utilized for arc fault detection.

###  III-B AI-Driven Approaches for Arc Fault Detection: Fundamentals and
Methods

The integration of sophisticated artificial intelligence methodologies,
including machine learning, deep learning, visual processing, and recognition
of patterns, facilitates the instantaneous examination of extensive datasets
produced by electrical systems and the detection of arc faults. In the past
couple of years, AI has been extensively utilized in various disciplines,
encompassing the field of electrical engineering [78, 79, 80, 81]. The
utilization of these AI-based techniques offers clear advantages over
conventional arc fault detection methods, particularly in terms of their
capacity to process immense quantities of data, high sensitivity in picking
small variations in an electrical waveform, and their compatibility with
existing electrical systems.

This section presents a brief overview of AI approaches highlighting their
significance in the present context and potential topics for future research.
The subsequent section of this discussion elucidates the use of AI techniques
in arc fault detection, so facilitating the reader’s understanding of its
enhanced precision and expedited outcomes.

####  III-B1 Machine Learning Based Methods

  1. i.

Support Vector Machines (SVM): The SVM uses Vapnik–Chervonenkis (VC) theory.
Next, Boser and colleagues presented a method that enhances training data
margin. The SVM finds the optimum hyperplane to divide data from two classes
with the highest margin and classifies data linearly or nonlinearly based on
features [82, 83].

  2. ii.

$K$-Nearest Neighbor (KNN): The $K$-nearest neighbor’s method was made by
Evelyn Fix and Joseph Hodges. The main idea behind the classification
algorithm is that an object is in the same class as its $K$ most similar
neighbors if most of those neighbors are in the same class [84].

  3. iii.

Random Forests (RF): Random forest is an ensemble learning method for
classification, regression, and other problems that builds many decision trees
during training and output [85]. Forests combine decision tree techniques,
utilize several trees, and improve a single random tree.

  4. iv.

Naïve Bayes (NB): NB is the simplest Bayesian network classifier [86]. NB
classifiers need linear parameters for the number of variables
(features/predictors) in a learning problem. Classifiers are usually built
using NB assigning class.

  5. v.

Decision Tree (DT): The decision tree model is very common among classifiers
[87]. DTs are well-liked due to their usefulness and simplicity. Additionally,
rules could be retrieved conveniently from DTs. The classification process is
performed using classification trees, which employ a top-down methodology by
dividing the input training data into smaller branches until a branch is
identified that exhibits the most pertinent label. The DT consists of a
central “root” node, multiple “nodes,” “branches,” and “leaves,” each
representing class labels.

Figure 12: The foundational architecture of LSTM.

####  III-B2 Neural network-based methods

  1. i.

Deep Neural Network (DNN): The structure of a DNN is made up of $n$
parameters, which are passed through a network with $N$ layers to get the
results and it is represented as:

$h_{1}=f_{1}(W_{1}^{T}\,X+b_{1})$ (10)

The input value is $X$, the first layer’s weight is $W_{1}^{T}$, and the first
layer’s bias is $b_{1}$. The first layer’s result is $h_{1}$, which is sent to
the second layer. In the case of multiple artificial neural networks, their
primary learning process involves assessing a single epoch and subsequently
adjusting the weight and bias values based on the error, to reduce the error
within each layer. This approach is commonly referred to as error
backpropagation. Neurons, weights, biases, and functions are the same
components of all neural networks, regardless of their shape. These components
could be trained just like any other machine-learning algorithm because they
function similarly to the human brain. The neurons that are located in the
layer below are connected to every neuron in this layer. It has the most
straightforward structure and serves as a crucial connection between all of
the neurons in the layers above and below. If all of the neurons in all of the
layers are fully connected (FC), the neural network is referred to as a DNN.
Fig. 11 illustrates the DNN’s structural layout. Supervised deep learning
approaches enable the construction of predictive algorithms for intricate
decision-making scenarios, such as the assessment of an object’s condition.

In the domain of fault detection, this condition is determined by the outcome
of a prediction of a potential failure or the identification of an already-
existent failure in a particular entity. In essence, a fault detection system
that utilizes deep learning techniques is employed in machine vision scenarios
and involves extensive data processing to detect and/or anticipate errors.

  2. ii.

Long Short-Term Memory (LSTM): LSTM is part of the recurrent neural network
(RNN) method. There are three gates in an LSTM unit: a “forget” gate, an
“input” gate, and an “output” gate. Through selective forgetting and
remembering mechanisms, these gate structures could get good feedback on the
right information, which helps the network get a better approximation of
complex nonlinear functions that change over time. In LSTM, the short-term
memory is handled separately from the long-term memory. The output of an LSTM
neuron is represented by the following equation:

$y_{t}=(\sigma\,(h_{t-1}\,x_{t}+W_{0}\,h_{t-1}+C_{t}\;C_{t-1}+b_{0}))\;\tanh(c_{t})$
(11)

where $\sigma$ is a variable that controls how much the weight and bias values
change based on the data received in one repetition (the learning rate).
$h_{t-1}$ and $h_{t}$ are the current and past states of short-term memory,
respectively. $C_{t-1}$ and $C_{t}$ are the current and past states of long-
term memory, respectively. $W_{0}$ and $b_{o}$ are the current cell’s weight
and bias, respectively. The data that come from another LSTM cell are
represented by $x_{t}$. The outgoing data, which is sent to another LSTM cell,
is shown by the variable $y_{t}$. The foundational architecture of LSTM is
shown in Fig. 12.

  3. iii.

Gate Recurrent Unit (GRU): GRU is another RNN algorithm. In GRU, long-term and
short-term memory are merged, unlike LSTM. GRU has two main gates: update and
reset. The update gate controls past state information flows into the present
state. The reset gate controls the ignored status information at the last
minute. The equation below represents the output of a GRU neuron:

$h_{t}=z_{t}\,\hat{h}_{t}+(1-z_{t})\,h_{t-1}$ (12)

Memory states at the past and current times are $h_{t-1}$ and $h_{t}$,
respectively. $z_{t}$, $\hat{h}_{t}$ represent the update gate and candidate
activation vectors.

  4. iv.

Gated Recurrent Neural Networks (GRNNs): have demonstrated their efficacy in
various domains that involve sequential or temporal datasets, including but
not limited to natural language processing, music synthesis, speech
recognition, machine translation, and classification tasks [88, 89].

  5. v.

Recurrent Neural Network (RNN): The RNN is commonly employed to characterize
sequential data. Currently, there are numerous effective applications of this
technology in various fields such as speech recognition, natural language
processing, medical diagnosis, fault diagnosis, and other domains. To gain a
comprehensive understanding of the training process of RNNs, it is crucial to
highlight the significant distinctions between feed-forward networks and RNNs.
Feed-forward networks are classified as stateless models, as they do not
possess dynamic states. This is because, throughout the training process, the
data is handled as static and does not involve any temporal dependencies.
Feed-forward neural networks are used to depict the correlation between
variables in each observation at a certain moment, without taking into account
the history. Therefore, significant links could be lost in certain cases as a
result of neglecting the influence of dynamic correlations between past and
present input and output data. The notion of “stateful” models was formulated
to effectively exploit the sequential dependencies of data pertaining to
previous inputs by persistently retaining the information in a “memory cell”
over a period of time [90].

  6. vi.

Convolutional Neural Network (CNN): The CNN architecture consists of a number
of interconnected network layers. The typical CNN architecture, as described
in reference [91], typically comprises an initial input layer, several
convolutional and pooling layers, one or two fully connected layers, and a
final output layer.

     * •

The primary function of the input layer is to receive input data within the
model. The quantity of neurons within this particular layer corresponds to the
overall count of features included in our dataset, specifically denoting the
number of pixels in the context of a picture.

     * •

The output received from the input layer is subsequently transmitted to the
hidden layer. The presence of numerous concealed layers is contingent upon the
specific model and magnitude of the data. Typically, the number of neurons in
each hidden layer tends to exceed the number of features. The calculation of
the output for each layer entails performing matrix multiplication between the
output of the preceding layer and the weights that are specific to that
particular layer, which could be adjusted through learning. Subsequently, the
learnable biases are incorporated, and an activation function is applied to
create nonlinearity into the neural network.

     * •

The output layer is responsible for receiving the data from the hidden layer
and applying a logistic function, such as sigmoid or softmax, to convert the
output of each class into a probability score representing the likelihood of
that class. The activation functions commonly utilized in diverse machine
learning models include rectified linear unit (ReLU), hyperbolic tangent
($\tanh$), and sigmoid whose curves are shown in Fig. 13.

Figure 13: Activation curve.

Several studies have utilized AI techniques for the purpose of arc fault
detection, as illustrated in Table II.

TABLE II: AI methods used for arc fault detection. AI Methods |  References |
---|---|---
Support Vector Machines (SVM) |  [92, 93, 94, 95, 96] |
$K$-Nearest Neighbor (KNN) |  [96] |
Random Forests (RF) |  [96], [77] |
Naïve Bayes (NB) |  [96] |
Decision Tree (DT) |  [96] |
Deep Neural Network (DNN) |  [97, 98, 99] |
Long Short-Term Memory (LSTM) |  [96],[100] |
Gate Recurrent Unit (GRU) |  [96] |
Gated Recurrent Neural Networks (GRNNs) |  [101] |
Recurrent Neural Network (RNN) |  [102] |
Convolutional Neural Network (CNN) |  [103, 104, 105, 106, 107] |

##  IV Challenges and Opportunities

The incidence of electrical safety accidents resulting from arc faults has
significantly contributed to the overall number of incidents and has exhibited
a progressive upward trend. The aforementioned phenomenon presents a growing
threat to both human lives and material possessions. The research of arc fault
detection technology has gained significant relevance and widespread societal
demand due to its crucial application background in addressing and mitigating
electrical mishaps caused by arc faults in residential buildings. The topic
exhibits a strong correlation with the safety of residential electricity, as
well as the overall safety of individuals and their belongings. Both local and
international investigations have shown favorable outcomes in the domain of
arc fault detection technology, as well as innovation, advancement, and
standardization of application products.

For accurately identifying the complexity, trends, and breakthroughs in the
context of arc fault identification, a thorough understanding of the
literature is essential. In addition to enhancing knowledge, a thorough
analysis of the existing literature could help reveal gaps and inequalities
that could be used to lay the groundwork for future research projects. A
comparison from the recent research articles has been done in the subsequent
subsections.

###  IV-A Challenges of Traditional Arc Fault Detection Approaches

Extensive research has been conducted in recent decades to investigate the
concerns and challenges associated with arc fault detection. Detecting arc
fault features involves defining criteria for physical values such as the low-
frequency harmonic value of the chirp zeta transform or the thermal features
generated by the arc. Nevertheless, monitoring the physical quantity of all
circuit components in real time poses a challenge in power distribution
systems that involve complicated loads. In recent years, the academic
literature pertaining to arc fault detection has mostly focused on two key
areas. One perspective to consider is the selection of suitable eigenvalues in
order to examine the underlying physical attributes of circuit faults. In
contrast, the investigation of data processing techniques is conducted with
the aim of attaining arc fault detection. In [108], a bandpass filter was
constructed to isolate arc signals within the frequency range of 2.4 to 39
kHz. Subsequently, the effectiveness of the db13 wavelet in analyzing the arc
characteristics was confirmed through the application of the discrete wavelet
transform method.

However, it was determined through experimental analysis that these
methodologies have limitations in accurately capturing the unique
characteristics of different loads. This is due to the dynamic nature of
electrical appliances, which are always evolving and introducing creative
features. This article in [109] introduces a detection methodology that
employs signal-type enumeration and a zoom circular convolution (CC) technique
(ZCC). The signal-type enumeration technique enhances the detection method’s
ability to generalize under unfamiliar conditions. The many components of
current with impulsive characteristics could be categorized into stable,
periodically impulsive, nonperiodically impulsive, and hybrid signals through
a process of enumeration. Given the constraints of CC, the ZCC technique is
introduced to extract discernible features and reduce the computational cost
associated with CC. Additionally, the signal function is partially recreated
to enhance the performance of CC. The primary challenge encountered during the
creation of AFCI is the selection of one or more appropriate criteria. Various
approaches for detecting arcing faults have been put forward in academic
literature, with spectrum analysis being the most commonly employed detection
technique. The fault detection technique proposed in [110] utilizes the
computation of the energy density spectrum and is designed to operate within
the framework of a pre-existing shunt active power filter architecture. The
methodology outlined in [111] examines the power spectrum density of
electromagnetic radiations.

A comprehensive understanding of the causal connections between problems and
symptoms exhibited by transformers is required. Therefore, many technicians
encounter difficulties while attempting to implement prior knowledge
approaches. In general, data-driven procedures tend to exhibit higher levels
of accuracy compared to knowledge-driven ones. The accuracy of physical models
is typically inferior to that of data-driven models. If sensors are put in a
manner inconsistent with the specified requirements, extensive changes will
invariably be required. Typically, it is advisable to have a qualified
professional perform the necessary modifications to equations and parameters.

Nevertheless, the conventional approaches encounter challenges such as the
complexity of setting thresholds, extensive computational demands, and limited
capacity for generalization when employing FFT, DWT, and other advanced
algorithms. To expedite the process of computation, it is necessary to employ
methods that yield rapid results. To achieve real-time detection, the time-
frequency information of the circuit is considered the primary selection for
fault feature value. A wavelet compression reconstruction-based data
enhancement technique addresses neural network overfitting caused by few
samples. Mathematical models pertaining to arc faults have been created based
on data from experiments. Models do not fully reflect the arc’s exterior
properties and are helpful for theoretical investigations only.

Moreover, there have been advancements in arc fault detection approaches that
rely on the identification of specific features associated with arc faults,
including strong luminosity, elevated temperatures, significant distortion
noise, and enhanced electromagnetic radiation [112]. However, a significant
limitation of these devices is their inherent inability to accurately
determine the precise locations of arc faults. In [113], the drop mode change
signals of voltage and current were detected using wavelet transform. The root
mean square of voltage and current was then retrieved as the indicators for
analysis. The techniques employed in this study are founded upon the wavelet
transform, a versatile mathematical tool that may be effectively utilized
across various load types, hence enhancing the accuracy and dependability of
detection mechanisms. Also, the aforementioned indicator threshold values are
established in an arbitrary manner, hence lacking both flexibility and
dependability in their determination. The methods for detecting series arc
faults, which rely on statistical analysis and signal processing, need a
substantial level of experimental expertise. These methods are most
appropriate for systems with relatively small sample sizes and a limited range
of load types. On the other hand, the utilization of neural network-based
series arc defect detection methods is more suited for extensive systems that
encompass a wide range of load types. In addition, it has been observed that
certain commercial AFCIs tend to either fail to activate when required or
activate erroneously. A previously conducted research study [114] reported an
approximate 50% accuracy rate in detecting arc faults. Consequently, the
correct detection of all arc faults in circuits remains challenging, and there
is a need for further refining of some detection methods, particularly for
circuits operating at 220-240 Volt.

###  IV-B Opportunities for AI/ML-Based Techniques

There is a growing interest in the utilization of machine learning/artificial
intelligence-based approaches for arc fault detection applications, primarily
driven by their efficiency and accuracy.Within the field of electrical system
protection, researchers have observed an important shift focused on the
incorporation of data analytics. The results of these attempts are remarkable.
Researchers are currently directing their efforts towards leveraging data
analytics in order to optimize the intelligence and effectiveness of
protection mechanisms within electrical systems. The convergence of
intelligent technology and data-driven insights holds great potential for
enhancing the effectiveness and dependability of electrical system protection
[115, 116, 117, 118, 119].

Recent research [103, 105, 120, 121, 107] has successfully demonstrated the
effectiveness of AI-based approaches in detecting series arc faults, yielding
excellent outcomes. Several research studies have been carried out to examine
the detection of arc faults. These investigations are classified according to
variations in voltage levels (high and low), current magnitudes (high and
low), applicative fields (residential and industrial), and the differentiation
between DC and AC. Upon reviewing the prior research, it has been noted that
the majority of studies primarily focus on utilizing arc data as current.
However, only one study has incorporated both voltage and current as arc data,
while another study solely employed voltage as arc data as shown in Table
IV-B1. According to the literature, researchers commonly employ both linear
and nonlinear loads in their studies. Some researchers exclusively focus on
nonlinear loads, while others solely investigate linear loads.

The impact of a series arc on line current varies depending on the load,
posing challenges in reliably extracting arc fault characteristics that are
applicable to all loads based on the line current signal [122]. As current
characteristics fluctuate with load, the generalization performance of the
determined detection method would be compromised when applied to undetermined
loads. Although current characteristics vary among different categories of
loads, even identical types of loads manifest distinct current
characteristics. In light of the current characteristics of diverse loads, it
is imperative to establish an adequate quantity of scenarios encompassing
various loads and choose an adequate number of features to identify faults
[107]. Hence, the literature has extensively examined arc fault detection
methods that have various types of loads, which are discussed below.

####  IV-B1 Combined Linear and Nonlinear Loads

The procedure of detecting arc faults in electrical systems encompasses the
identification of potentially dangerous electrical arcs within a system that
has a combination of linear and nonlinear loads. Linear loads provide a steady
and predictable current draw pattern that closely resembles a sinusoidal
waveform. Nonlinear loads, which are prevalent in contemporary electronic
devices, exhibit a more intricate and non-sinusoidal current draw pattern. The
identification of arcs under complex load circumstances poses a substantial
obstacle. Linear loads have the capacity to induce anticipated disruptions in
the waveform of the current, whilst nonlinear loads possess the capability to
introduce unforeseeable fluctuations. Detection methods must take into
consideration the wide range of diversity present. Examples of linear and
nonlinear loads are incandescent lights, resistive heaters, motor-type loads,
power electronic loads, microwave ovens, and similar electrical appliances
[123, 107, 121].

An all-encompassing strategy could involve the utilization of specialized
sensors and monitoring equipment for the continuous analysis of electrical
waveforms. Machine learning algorithms have the capability to undergo training
in order to distinguish between typical load fluctuations and typical patterns
that signify the presence of an arc. To achieve accurate identification, the
model must be capable of adapting the diverse properties exhibited by both
types of loads. The implementation of this integrated technique improves
safety by effectively detecting potential arc faults within a system that
encompasses a variety of load types.

The primary focus of studies in studying arc faults in both linear and
nonlinear loads has been largely centered on scenarios employing AC. In this
particular field, a significant portion of research has focused exclusively on
low-current conditions. On the other hand, a certain group of researchers has
focused their efforts on examining DC situations.

  * •

AC Arc Fault Detection: ArcNet convolutional neural network-based arc
identification model is proposed in [124]. They detected AC series arc fault.
The database includes two of the most frequent forms of arcs: those resulting
from loose cable connections and those arising from insulation failure, both
of which are generated during testing. According to test findings, ArcNet
could recognize arcs with the greatest precision accuracy of 99.47% at a
sampling rate of 10 kHz while using a database of raw current. The high-
frequency coupling convolution neural network (HCCNN) approach for the
detection of a series arc fault is presented in [125]. This sensor collects
high-frequency feature signals from different types of loads in a series arc
and regular operation states. The temporal-domain sequence converts the signal
to two-dimensional feature grayscale images. These photos are used to train
and assess a three-layer convolution neural network using a number of common
loads’ arc conditions and normal conditions data sets. It detects the load
type and series arc simultaneously and accurately. DWT in conjunction with a
DNN is proposed in [97]. An experimental bed was developed to take the current
signals under normal and arcing states. The current properties of arc faults
were analyzed, and afterward, wavelet coefficient sequences were utilized to
construct the input matrix for a deep neural network. A hybrid combination of
principal component analysis and support vector machine (PCA-SVM) is proposed
for series arc detection [72]. Accuracy levels for load identification and
series arc identification are 99.1% and 99.3%, respectively. To examine the
properties of DC arc faults, a DC arc generator has been designed and is
depicted in Fig. 14.

Figure 14: Laboratory arc generator setup used in laboratories for
experimental purposes.

A residual network (ResNet)-based arc fault detection model and data
enhancement method are presented in [98]. An enhanced Mel-Frequency Cepstral
Coefficients (MFCC) for preprocessing and a neural network model named ARCMFCC
is proposed for identifying arcs to attain the highest level of accuracy in
detecting arcs that is 99.34% [123]. A novel approach was proposed for
detecting series arc faults and selecting phases, utilizing single-phase
current [94]. The integration of the Fractional Fourier Transform (FRFT) and
the 2-level block Singular Value Decomposition (SVD) is highly effective in
extracting arc fault detection and achieving phase selection. In [104]
outlines a way for finding arc fault that combines the residual network
framework and the channel attention technique. This research presents a novel
module called one-dimensional depth separable convolution (1D-DS) that tries
to decrease the number of variables in a network model and lower the inference
time for individual prediction samples. The accuracy of their model was found
to be 98.07%. In [107] presents a novel approach for the detection of series
arc defects utilizing a specific mechanism, a high-frequency (HF) RLC AM and a
one-dimensional convolutional neural network (1DCNN). Better generalization
and 99.33% average detection accuracy are achieved with the provided method
compared to the 1DCNN method using limited real-current data. The architecture
of the presented 1DCNN is depicted in Fig. 15.

Figure 15: Structure of the proposed 1DCNN [136].

In [107] describes a technique for detecting series arc defects utilizing an
HF RLC AM and a 1DCNN. Better generalization and 99.33% average detection
accuracy are achieved with the provided method compared to the 1DCNN method
using limited real-current data. The research study in [126] introduces a
novel approach for identifying series arcing problems inside AC residential
electrical systems. The method under consideration is founded on the
utilization of a Kalman filter for the purpose of detecting fault indications.
Additionally, a decision block is incorporated into the algorithm to validate
the existence of a series arc fault and then trigger a tripping signal. The
estimation of the current at one end of the power line is conducted using a
model that incorporates two steady-state variables, namely X1 and X2. The
input parameters for failure symptom detection in a Fuzzy logic processor
include residuals and the third-order difference of state X2. Additionally,
the fault indications are analyzed by a detecting logic block, which verifies
the existence of an electrical arcing problem. In [127], the Mann-Kendall Test
is used to compare two nearby cycles’ current waveforms rather than directly
using them. The challenge of arc localization is converted into an
optimization problem, wherein a genetic algorithm (GA) is employed to maximize
the findings of differential current decomposition and accurately determine
the location of the arc fault. In order to identify residential AC series arc
faults, the research in [128] suggests a sparse representation and fully
connected neural network (SRFCNN) approach. The SRFCNN approach employs neural
networks (NN) to carry out adaptive feature learning and classification while
capturing the characteristics of conventional signals using sparse coding. A
preprocessing layer, a sparse representation layer, and a decision layer make
up the methodology’s intricate organizational structure. The following main
steps make up the processing process. Prior to selecting a fixed vocabulary, a
collection of customized bases is first constructed via dictionary learning to
act as accurate feature descriptors. Accordingly, a set of sparse coefficients
is created from the pretreatment input samples, which are subsequently fed
into the NN for identification. In [129] methodology for the detection and
identification of arc faults in low-voltage users at the service entry. The
approach that has been suggested utilizes supervised, non-intrusive current
disaggregation. The suggested methodology involves the utilization of a Non-
Intrusive Load Monitoring (NILM) module to detect and identify the appliances
that are currently in operation within a domestic dwelling. Additionally, an
arc fault detection module is employed to examine the simultaneous total
current signal for the presence of an arc fault. If an arc fault is
identified, a module for arc current disaggregation will employ total current
harmonic disaggregation to identify the most suitable arc fault waveform
associated with one or many individual appliances. The classification
technique known as NILM could be categorized as either supervised or
unsupervised, based on the type of data utilized during offline training to
provide prior class information for subsequent prediction[137].

In [105], a method for arc defect identification is based on ensemble learning
and decision-level feature fusion. The effectiveness of the multimodal feature
fusion technique in detecting arc faults surpasses that of using single-mode
features alone. In a study using 1000 randomly selected samples, the average
detection rate achieved was 98.87%, with the highest detection rate of 99.30%.
The present study introduces a novel methodology referred to as the temporal
domain visualization convolutional neural network (TDV-CNN) in the manuscript
[106]. The current transformer and high-speed data collecting system are
employed for the purpose of gathering the current measurements pertaining to a
succession of arc faults. Subsequently, the acquired signal undergoes digital
filtering and is subsequently transformed into a grayscale image in
chronological order prior to being inputted into the TDV-CNN. The experimental
findings validate that the classification accuracy could attain a level of
98.7% or perhaps exceed it with optimal parameter adjustments. The
implementation of a detection methodology that solely depends on the zero-
current time proportional coefficient or the normalized mean square error
coefficient leads to the presence of both false alarms and missed alarms when
exposed to different types of loads, such as workstations and fluorescent
lighting. The approach employed by the researchers in [130] offers a distinct
benefit as it incorporates the fusion of many features associated with arc
faults rather than merely enumerating these features. They have used fuzzy
logic for arc identification. Consequently, this approach exhibits a notable
level of accuracy in the detection of arc faults.

The non-stationary and nonlinear nature of low-voltage series arc fault
currents needs careful consideration of fault features and the adaptation of
the detection algorithm to get accurate detection results. In light of the
aforementioned concerns, a novel approach called ECMC is introduced by authors
in [131], which integrates Euclidean distance, classifier criterion, max-
relevance min redundancy(mRMR), and clustering index as a feature selection
method having optimal feature subset Fb. This approach has the capability to
eliminate features with weak correlations and duplicate information in the
domains of time, frequency, and time-frequency. The feature data set created
by ECMC is able to comprehensively capture the distinctive attributes of arc
faults. The suggested approach introduces a stochastic configuration network
(SCN) that utilizes variational Bayesian (VB) optimization, referred to as VB-
SCN. To iteratively improve the output weights and scale functions of the SCN,
the suggested method makes use of VB. The utilization of VB-SCN has the
potential to enhance the overall quality of hidden-layer nodes inside a
network, as well as improve its generalization ability. This is achieved by
simultaneously ensuring the adaptive learning of current characteristics,
ultimately resulting in a faster and more stable arc defect detection process.
The flow chart of the ECMC feature selection approach is depicted in Fig. 16.

Figure 16: Flow diagram depicting the ECMC feature selection approach [131].

The study presents in [77] a novel technique for detecting and locating series
arc faults in multi-load circuit topologies, with a particular focus on branch
arc faults and nonlinear power loads. This study examines the current
fluctuations resulting from the occurrence of arcing at various locations in
paralleling coupled loads. In this study, a feature selection technique
utilizing the RF algorithm is employed to identify particular feature sets
based on the minimization of Gini impurity. The DNN utilizes a selection of
the top ten features that exhibit a strong correlation with the arc. These
features are chosen based on their integration and are inputted into the DNN
for calculation and training, considering various combinations of loads. The
author of [138] suggests that the connection with SimulAI enables the seamless
processing of input data derived from simulators as well as actual devices,
such as sensors and cameras. Furthermore, the inclusion of modularization
provides the opportunity for interaction with other elements in a closed-loop
manner. A study in [95] proposed a data-driven approach for identifying low-
energy arc-faults. However, this approach was limited to AC circuits and did
not differentiate between low-energy arc-faults and stable arc-faults.
Additionally, the trained model was not evaluated in a real-world setting due
to the intricate nature of the preprocessing steps required for the SVM
classifier[95]. Table IV presents a comparison of the approaches employed by
different researchers in analyzing both linear and nonlinear loads.

TABLE IV: Comparison of methodologies used for both linear and nonlinear loads Reference |  Load | Methodology |  Accuracy |
---|---|---|---|---
[124] | 4 | CNN | 99.47% |
[125] | 8 | HCCNN | 99.20% |
[97] | 4 | DWT and DNN | 97.75% |
[98] | 6 | Residual Network (ResNet), DNN, wavelet compression reconstruction | 97.69% |
[123] | 12 | MFCC and NN | 99.34% |
[121] | 2 | Composite bandpass filter (CBF), principal component analysis (PCA), temporal convolution network (TCN) | 99.88% |
[107] | 3 | 1-DCNN and RLC-based AM | 99.33% |
[126] | 7 | Kalman filter and fuzzy logic processor | - |
[127] | 3 | Mann–Kendall test and current decomposition | 92.86% |
[128] | 6 | Sparse representation and fully connected neural network (SRFCNN) | 94.30% |
[129] | 5 | Non-intrusive load monitoring (NILM) and arc current disaggregation | 94.86% |
[105] | 6 | 1-D CNN | 99.30% |
[106] | 10 | TDV-CNN | 98.70% |
[130] | 7 | Current fluctuation and zero-current features | - |
[77] | 4 | Random Forest (RF) - DNN | 97.50% |
[131] | 4 | ECMC and VB-SCN | 98.25% |
[95] | 12 | Wavelet-analysis-based singular-value-decomposition (WASVD), SVM | 95.80 % |
[132] | 9 | ICEEMDAN and LightGBM algorithm | 97.06% |
[122] | 7 | Wavelet transform, genetic algorithm and ANN | 99.21% |
[120] | 3 | Ensemble Machine Learning (EML) | 99.80% |
[133] | 2 | Transformer Neural Network (TNN) | 96.30% |
[101] | 4 | LG-RNN | 98.27% |
[96] | 3 | SVM, random forest, k-nearest neighbor, Naive Bayes, decision tree, LSTM, GRU, DNN | 95.00% |
[99] | 10 | Wavelet transform and DNN | 98.70% |

This study in [132] involves the acquisition of arc fault signals from a
multi-branch experimental platform that has been developed with masking loads
in accordance with the specifications outlined in IEC 62606. The inclusion of
adaptive noise in the ICEEMDAN algorithm has been determined to be an
appropriate approach for extracting a greater amount of comprehensive arc
fault information. The IMF is chosen, and its variance is computed to create
detection variables that could differentiate between the occurrence of an arc
fault and the normal operating condition. On average, the capability to gather
arc fault information is enhanced by a factor of 8.47 when comparing it to the
wavelet transform. The utilization of Boruta and LightGBM algorithms is
employed to enhance the effectiveness of the algorithm and streamline its
structure by reducing the dimensionality of the features. The presented
approach for arc fault detection effectively and efficiently detects multi-
branch arc faults using a well-trained LightGBM model. The Raspberry Pi
platform has been demonstrated to achieve an identification accuracy of 97.06%
and a detection time of less than 300 ms.

This study in [122] examines the attributes of several load categories in the
presence of a defective arc. Subsequently, a technique for detecting series
arc faults is introduced, which relies on category identification and
artificial neural network techniques. Initially, the loads are classified into
two levels depending on the voltage waveforms and current waveforms.
Subsequently, quantifiable measurements are chosen by the distinctive
characteristics exhibited by various load categories. The data pertaining to
indicators is utilized to train an artificial neural network that corresponds
to the type of loads. This trained network is subsequently employed in the
detection of arcs. By the designated time frame outlined in the detection
criteria, each occurrence of an arc is recorded and tallied by incrementing
the arc count by one. Ultimately, the cumulative count result is obtained by
integrating the individual arc counts. When the number of counts reaches the
specified threshold outlined in the norms, it is considered to indicate an arc
fault has occurred. Consequently, the protective device promptly interrupts
the circuit. The authors in [133] suggest a deep learning strategy devoid of
descriptions. To solve this time series issue, they modified a sequence-based
model known as a Transformer Neural Network (TNN) model. They used a sequence-
to-sequence model to reuse the transformer’s encoder. A window of electric
current with a minimum of one period of the signals is provided as input to
the model. The labels for every single point in the input window are the
output. This necessitated coming up with a novel way to classify the signals,
therefore creating an automated technique to increase training supervision.
Their model had a 2% false positive rate and a 96.3% identification accuracy.
Certain researchers have put forth the idea of utilizing a neural network-
based sensor for the purpose of detecting DC arc faults. Initially, an arc
signal acquisition module is developed using electromagnetic induction. This
module is designed to automatically adjust the sample frequency and accomplish
signal amplification. The maximum achievable sampling frequency is 4 MHz. In
[96], they have two types of scenarios in which test data of a category might
be classified. The first scenario occurs when the test data has already
undergone training, whereas the second scenario occurs when the test data has
not undergone training. The aforementioned scenarios are commonly categorized
as enclosed kinds and unenclosed types, respectively. Various artificial
intelligence methods, including machine learning and deep learning algorithms,
were employed in the study. The results indicated that the performance of
different AI systems in arc identification varied significantly. Machine
learning algorithms provide strong performance in the low-frequency region;
nonetheless, they necessitate the extraction of relevant features. DL
algorithms typically require a significant quantity of data in order to
achieve optimal performance. The selection of an appropriate technique is of
paramount importance in the creation of a dependable and resilient arc fault
detection method. A novel neural network approach is proposed in [99], which
incorporates wavelet analysis and feature value decomposition. The data has
been divided into five layers using the DB5 wavelet base. The coefficients of
the first layer are then extracted in order to generate the Hankel matrix. The
initial process of feature extraction and compression has been completed. Upon
performing Eigenvalue Decomposition (EVD) on the matrix, the eigenvector is
generated, completing the second phase of feature extraction and data
compression. The third stage of feature extraction and data reduction is
achieved by computing the mean, root mean square, and standard deviation
values of the eigenvector. The DNN fault detection model is trained using
three input values with a compressed input scale of 1 × 3. This study
introduces a deep neural network (DNN) defect detection model that exhibits
low complexity and high timeliness. The complicated nature of the algorithm
has been significantly decreased in terms of both operating data and the
network’s architecture, resulting in a training time of around 33 seconds. The
time-efficient model detects faults using current half-cycles. An overall
identification rate of 98.7% was achieved. • DC Arc Fault Detection: In [121],
a technique to speed up feature extraction while maintaining accuracy is
proposed. First, DC arc datasets are created using a fully automated process.
The usual frequency segment of a DC arc is designed to be extracted using a
composite bandpass filter. Additionally, a temporal convolution network-based
arc detection neural network is presented to extract current waveform
properties. These features are processed using principal component analysis to
lessen correlation. Finally, the classifier is a single hidden layer neural
network. The database has been assembled from various working situations and
scenarios. At a sampling rate of 250 kHz, the arc fault detector could measure
DC raw current with a test set accuracy of 99.88%. With an average real-time
detection of 0.15 seconds per sample, the model is also implemented on the
Jetson Nano. In [120] introduces an efficient and adaptable method for
detecting series DC arc faults using ensemble machine learning (EML) methods.
A buck converter and a boost converter, both serving as constant power loads
(CPLs), have been conceived and constructed to investigate various data to
train machine learning approaches and arc fault behaviors. The experimental
data is used to extract a set of time domain features, which are then analyzed
using the feature significance attribute. The characteristics were further
processed by an adaptive normalization method to address the issue of false
positive categorization resulting from load changes. The suggested methodology
involves a two-step algorithm for the identification and detection of arc
faults across various load categories. Numerous pre-processing (descriptors)
linked to a machine learning model (or deep learning) are used by the majority
of detection approaches in the detection literature. The descriptors for these
methods must be manually created. Additionally, the authors in [101] suggest a
novel approach called the Leftover Gated Recurrent Neural Network (LG-RNN)
which aims to extract comprehensive features from the contextual information
and afterward carry out classification tasks. Fig. 17 illustrates the
experimental configuration commonly employed by researchers for investigating
series arc faults. Figure 17: Experimental configuration for a series arc
fault.

####  IV-B2 Nonlinear Loads

The method of arc fault detection in the context of nonlinear loads entails
the identification of potentially hazardous electrical arcs that may arise
within systems operating with nonlinear loads. Nonlinear loads, such as those
encountered in contemporary electronic devices, exhibit current consumption
patterns that deviate from a straightforward sinusoidal waveform. These loads
could generate unpredictable and complex electrical arcs.

The identification of arcs in nonlinear loads necessitates the utilization of
sophisticated techniques. One methodology is the examination of the present
waveform and the identification of anomalous patterns that signify the
presence of an arc. Machine learning and AI algorithms have the capability to
undergo training in order to identify these patterns and subsequently activate
alarms or implement precautionary measures upon the detection of an arc. For
nonlinear loads, arc fault detection is divided into two types of situations:
AC and DC. The researchers’ main focus is on low-current circumstances. The
existence of nonlinear loads introduces intricacy to the identification
procedure due to the significant variability in their current waveforms.
Sophisticated algorithms are required to effectively distinguish between
regular load fluctuations and potentially hazardous arc incidents. Examples of
nonlinear loads are three-phase motors, inverters, power electronics loads,
vacuum cleaners, induction cookers, and similar electrical appliances[134,
104, 100].

  * •

AC Arc Fault Detection: The adaptive arc identification system has been
enhanced and is now accessible, leveraging machine learning techniques. The
SVM has strong robustness and possesses the ability to efficiently acquire
knowledge and categorize limited sample sizes. Arc fault detection is
accomplished by providing spectral characteristics or other relevant
parameters as input to the SVM [134]. This study presents a recognition
approach that utilizes kernel principal component analysis (KPCA) and firefly
algorithm optimized support vector machine (FA-SVM). KPCA was employed as a
method to effectively distinguish between the harmonics and load noise
disturbances present in the voltage and current signals. The kurtosis and
skewness of the 5th and 6th major components were utilized as arc fault
characteristics. The FA-SVM was developed with the purpose of detecting and
identifying arc faults. In [102], a series arc fault detection and line
selection technique for a multi-load system based on RNN was put forward. The
authors developed a method for quick and continuous detection, as well as a
method for correcting classification results based on probability which gave
them an accuracy of 98.7%. A novel SAF detection model using a CNN was
introduced to efficiently and precisely detect the series arc fault in a
three-phase motor with a frequency converter load (TMFCL) circuit. The point-
by-point isometric mapping technique was introduced as a means to generate the
input matrix. The model’s lightweight architecture was achieved through the
utilization of constraint building blocks and depth-wise separable
convolutions. The level of difficulty and expected performance of convolution
operators was conducted using a roofline model. Based on an analysis of the
operators’ runtime, the researchers in [103] identified and designated the
most efficient lightweight SAF identification model as SAFNet. In [94] A novel
approach was proposed for detecting series arc faults and selecting phases,
utilizing single-phase current. The integration of the Fractional Fourier
Transform (FRFT) and the two-level block SVD is highly effective in extracting
arc fault detection and achieving phase selection. A way for finding arc fault
that combines the channel attention mechanism and the residual network model
is outlined in [104]. This research presents a novel module called one-
dimensional depth separable convolution (1D-DS) that aims to decrease the
number of parameters in a network model and lower the inference time for
individual prediction samples. The accuracy of their model was found to be
98.07%.

  * •

DC Arc Fault Detection: The authors in [100] put forward a two-stage training
strategy for a transfer learning-based low-energy arc-fault detection network
(TL-LEDarcNet) to identify series DC arc-faults. A lightweight one-dimensional
convolutional neural network and a one-layer LSTM network were created to
detect low-energy arc faults using only sensed current information and got an
accuracy of 95.8%.

Table V displays the comparison of approaches employed by various researchers
about nonlinear loads.

TABLE V: Comparison of methodologies used for only nonlinear load Reference |  Load | Methodology |  Accuracy |
---|---|---|---|---
[134] | 3 | KPCA and FA-SVM | 97.17% |
[102] | 1 | RNN | 98.70% |
[103] | 1 | CNN | 99.44% |
[94] | 8 | SVM and PSO | 97.53% |
[104] | 6 | CNN | 98.07% |
[100] | 1 | LSTM | 95.80% |

####  IV-B3 Linear Loads

Linear loads refer to electrical devices that exhibit a direct proportionality
between the current flowing through them and the voltage applied across them.
Examples of linear loads are incandescent lights, resistive heaters, and
similar electrical appliances. The primary difficulty associated with arc
fault identification in the presence of linear loads is the differentiation
between typical electrical variations and hazardous arc faults that have the
potential to cause fires or pose other safety risks. The identification of
characteristic waveforms or patterns in electrical signals is of utmost
importance in the early detection of arc faults.

  * •

DC Arc Fault Detection: In [92], the system utilizes arc time-frequency
signatures obtained by a modified empirical mode decomposition (EMD) technique
and employs the SVM method for making decisions. The current data in the time
domain for load arc faults is subjected to multilayer discrete wavelet
analysis. A coefficient matrix is created from five-layer discrete wavelet
detail values and translated into an RGB phase space image using a colormap
index. They introduced wavelet compression reconstruction-based data
improvement.

  * •

AC Arc Fault Detection: A novel method for diagnosing arc defects is presented
that incorporates SVM, improved multi-scale fuzzy entropy (IMFE), and
variational mode decomposition (VMD) techniques [93]. Using VMD, the current
signal is decomposed into intrinsic mode functions (IMFs) in the time-
frequency domain. The associated IMFs with the arc defect are then utilized to
calculate IMFE. Finally, SVM detects IMFEs utilized for arc faults. The
article [135] focuses on the examination of two primary events: the arc
results from a tree’s inclination on a medium-voltage distribution line, as
well as the arc that arises between sphere gaps. The examination of real-time
arc signals is conducted using a combined approach of EMD and ANN. The
utilization of EMD and ANN in the analysis of arc voltage data has proven to
be effective in accurately identifying and categorizing arcing occurrences
based on their main harmonic characteristics. They got their highest accuracy
of 100% and lowest accuracy of 98.2%.

Table VI displays the comparison of approaches employed by various researchers
about linear loads or no load.

TABLE VI: Comparison of methodologies used for linear load and no load References |  Load Type | Methodology |  Accuracy |
---|---|---|---|---
[92] | Resistive | EMD and SVM | 96.00% |
[93] | Resistive | VMD, IMFE, and SVM | 99.00% |
[135] | No Load | EMD and ANN | 98.20% |

It is worth noting that there is specific research that demonstrates a higher
level of accuracy in detecting DC arc faults. However, it is evident that
there is a lack of similar discussions on AC arc faults. In the same way,
while many researchers have engaged in discussions on the accuracy pertaining
to low-voltage situations, a corresponding examination within the framework of
high and medium-voltage settings is conspicuously lacking in the existing body
of research.

###  IV-C Limitations of Artificial Intelligence and Machine Learning
Approaches

The utilization of AI and ML in the domain of arc fault detection is subject
to several constraints, which include difficulties pertaining to the quality
of data, intricacy of models, ability to generalize, interpretability, and the
requirement for continuous model maintenance. The restricted interpretability
of outcomes is dependent upon the quality and amount of the available data. In
addition, the system encounters several hurdles in effectively resolving
complex or uncommon problems, is vulnerable to the presence of irrelevant or
extreme data points, and experiences difficulties in determining appropriate
algorithms and parameters[139]. The transmission of knowledge between
disparate areas poses a significant challenge. The reliance on substantial
quantities of precisely annotated data is a crucial determinant [140]. The
issue of sensitivity to hyperparameter selection holds great importance.
Within the domain of machine learning, a significant issue arises about the
possibility of algorithms becoming overfitted to the training data [68]. The
recognition of these limits is of utmost relevance for stakeholders operating
in safety-critical industries, as it provides guidance for the development and
deployment of robust and effective arc fault detection systems.

##  V Conclusion

The aim of this systematic study is to offer a detailed examination of the
most recent advancements in approaches for detecting arc faults. This review
comprises many approaches, including time-domain examination, frequency domain
examination, and time-frequency domain examination, alongside the analysis of
physical events and the utilization of artificial intelligence. The present
review centers on the incorporation of AI in the context of arc fault
detection. The integration of AI methods has demonstrated various benefits,
such as the capacity to analyze substantial volumes of data in real-time with
enhanced precision, quicker reaction times in the detection of arc faults, and
the potential to learn for identifying particular fault types.

Despite the numerous advantages associated with artificial intelligence, it is
essential to acknowledge and address the limitations that exist within current
arc fault detection approaches. The implementation of deep learning methods on
embedded devices is often challenging due to the intricate network
architecture, numerous parameters, and extensive computational requirements.
To achieve optimal accuracy, it is important to construct an extensive
database. To address the issue of limited data availability, it is imperative
to create effective techniques for data gathering and preparation. Fault
detection techniques that rely on data are vulnerable to anomalies caused by
factors, for instance, measurement problems, disruptions caused by machines,
and the start-up procedure for loads. These anomalies could lead to the
generation of unfavorable data, which hinders the accurate detection of
abnormalities.

AI methods have demonstrated superior accuracy compared to other approaches.
Nevertheless, it is important to acknowledge that these techniques necessitate
a substantial volume of data for the training process and rely on significant
computational resources. In contrast, classic methods such as the over-current
and wavelet-based approaches offer advantages in terms of simplicity and
speed, respectively. Hybrid methods have been developed as an initiative to
integrate the benefits of different approaches. These procedures are
characterized by their increased complexity, yet they yield exceptional
outcomes.

As the volume and quality of data rise, the effectiveness of data-driven
processes is enhanced. In cases where a system possesses a comprehensive and
dependable model, model-based approaches would be more advantageous
alternatives. This study facilitates the user in identifying the most
efficient approach for fault detection and exploring different techniques to
enhance the system’s capacity.

Further investigation should be conducted into the utilization of synthetic
data generation and transfer learning techniques as a means to enhance the
training dataset. Furthermore, the combination of various artificial
intelligence methodologies and the incorporation of expert knowledge have the
potential to enhance the precision of fault detection and mitigate the
occurrence of false alarms. It is worth studying algorithms that could
effectively and efficiently operate on hardware with limited processing
capability. In conclusion, it could be inferred that artificial intelligence
holds significant potential in changing the domain of arc fault detection,
providing a beneficial and precise methodology to address the crucial issue of
safeguarding electrical safety.

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