A Bibliographical Survey on Fault Detection, Classification and Location Methods in Power Systems Using Artificial Intelligence

2.1. Artificial Intelligence Concepts

Alan Turing proposed in 1950 the idea of solving problems using machine intelligence [57]. The term Artificial Intelligence was coined by J. McCarthy, M.L. Minsky, N. Rochester, and C.E. Shannon in 1956 [58]. Although there is not a general accepted definition of AI, many definitions have been proposed. For instance, Russell and Norving define AI as “any entity that perceives its environment from sensors and acts in that environment from actuators can be described as an agent” [59]. An AI system may be seen as a system that simulates the functioning of the human mind, making computers, robots, and software think intelligently like humans. Since the birth of AI in the 1950s, the development of intelligent machines has been driven by a diverse array of approaches such as statistical learning, knowledge-based systems or soft computing [60]. Currently, there is a huge number of publications available for those interested in this subject. References [59,60,61,62,63,64,65,66,67] are a small sample of items that can be of help.

Actually, AI is an umbrella term that covers a variety of interrelated, but distinct, fields. Three main AI fields have been applied to power system studies [61]:

• Machine Learning (ML) is a subfield of AI focused on training algorithms with data sets to produce models capable of performing complex tasks [68,69,70,71,72]. Actually, an ML model is a computer program that can be used to recognize patterns in data or make predictions.

Three types of ML algorithms can be distinguished:

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Supervised learning: An algorithm is trained using data tagged with a label so that an algorithm can successfully learn from it. Training labels help the model know how to classify data in the desired manner. Two types of models can be distinguished in this group: classification and prediction. Classification models are used to assign test data into specific categories. Regression models use an algorithm to understand the relationship between dependent and independent variables, and can be used to predict numerical values from different data points.

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Unsupervised learning: Unlabeled data are used to train an algorithm; the algorithm finds patterns in the data itself and creates its own data clusters. Unsupervised learning can be used to find patterns in data that are currently unknown.

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Reinforcement learning: It is a technique in which positive and negative values are assigned to desired and undesired actions. The goal is to encourage programs to avoid the negative training examples and seek out the positive learning how to maximize rewards through trial and error.

Some authors add other types:

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Semi-supervised learning: It uses a mix of labeled and unlabeled data to train an algorithm. In this process, the algorithm is first trained with a small amount of labeled data before being trained with a much larger amount of unlabeled data.

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Ensemble methods: They make use of several ML algorithms to improve the performance that can be achieved with the use of a single algorithm. Ensemble learning constructs a set of hypotheses generated by several base learners that are used together to solve a single problem and provide better generalizability than individual base learners.

Before training an ML algorithm, the so-called hyperparameters must be set; they act as external guides that inform the decision process and direct how the algorithm will learn. The number of branches on a regression tree, the learning rate or the number of clusters in a clustering algorithm are examples of hyperparameters. Hyperparameters train and direct the algorithm; parameters begin to form in response to the training data. These parameters include the weights and biases formed by the algorithm as it is being trained.

Figure 2 depicts a list of ML techniques classified according to the categories mentioned above; see also [23]. For a definition and description of these techniques see the recommended literature [59,65,66,67,68,69,70,71].

• Metaheuristic methods are a type of algorithm characterized by their ability to solve optimization problems by mimicking natural phenomena or human intelligence [72,73]. They are used to find approximate optimal solutions to complex and highly nonlinear optimization problems for which no deterministic approach is able to handle in an acceptable amount of time. The particle swarm optimization (PSO), the ant colony optimization (ACO), the genetic algorithm (GA), the tabu search method or the simulated annealing method are some of the most popular algorithms. A significant effort has also been carried out to review this group of methods, see references [74,75,76,77,78,79,80,81]. It is worth mentioning that reference [79] provides a list of more than 500 metaheuristics algorithms, and it is not complete since some additional methods have been proposed; see, for instance, [82].
• Rule-based systems, also referred to as expert systems (ESs), are a group of techniques that allow the direct integration of human knowledge. An expert system is a computer software that emulates the process used by human experts when they solve problems [83]. By developing a set of if-then rules, the system is able to decide based on the rules given by an expert. Besides the Boolean logic, fuzzy logic has also been used in rule-based systems; its main advantage is the description of variables and relations in human linguistics. A fuzzy system normally consists of three basic parts [84]: (1) fuzzification, where the input signals are mapped onto a fuzzy membership function using a membership degree; (2) inference, where the calculated degrees of membership are integrated into IF-THEN fuzzy rules; (3) defuzzification, which creates an output signal that the physical system is able to handle. For more information on this AI subfield, see [85,86,87,88,89].

Figure 3 shows a diagram with the different AI groups and subgroups mentioned above.

Deep Learning (DL) is a subset of ML, in which artificial neural networks (ANNs) are used to perform more complex reasoning tasks without human intervention. The main differences between DL and ML are the neural network architecture, how each algorithm learns, and how much data each type of algorithm uses. Non-deep traditional ML models use simple neural networks with one or two layers, DL models use three or more layers (typically hundreds or thousands of layers) to train the models. Figure 4 schematizes the relationship between AI, ML and DL. While supervised ML models require structured labeled input data, DL models can use unsupervised learning. With unsupervised learning, DL models can extract the characteristics, features and relationships they need to make accurate outputs from raw unstructured data. DL automates much of the feature extraction piece of the process, eliminating some of the manual human intervention required. In general, a DL model requires more data values than an ML model to improve accuracy. A significant number of publications is available to readers interested in this subject; see, for instance, [90,91,92,93,94,95,96,97,98].

Natural Language Processing and Robotics are two fields also covered by the AI umbrella.

2.2. Fault Diagnosis Concepts

A first aspect to be clarified is the distinction between failure and fault, two terms that are often indistinctively used.

A failure of a power component may be defined as an inability condition of that component to perform required functions on demand due to one or more defects. A failure of a component is, therefore, the termination of its ability to perform a required function [99]. That is, a component has suffered failure when it is no longer capable of fulfilling one or more of its intended functions; this does not mean that it is completely unable to function.

The cause that initiates the failure is the failure cause, while the physical process that leads to the failure is the failure mechanism. A failure can cause loss or damage to other power components. The final consequence of component failures is known as outage when a disruption occurs in either generation, transmission or distribution of electricity, causing a loss of power to some or all customers in the system [99].

A component failure might result in a fault. A fault in a power system can be defined as an abnormal electrical condition which interferes with the normal power flow and may affect the capability of the power system to fulfill its function [99]. A fault is a defect in the electrical circuit due to which current is diverted from the intended path and that can result in damage to other power components. A fault is a defect that causes a component to fail or act abnormally and may lead to a failure.

Note that the two definitions are circular: a failure may lead to a fault, a fault may lead to a failure. Actually, this is not contradictory. Consider the following example: an overvoltage causes an insulation breakdown to a non-properly protected underground cable (i.e. the overvoltage is the cause, the insulation breakdown the mechanism of the failure); this failure causes a fault that increases significantly the system current, which in turn may cause a failure to other components affected by the overcurrent.

When a fault occurs in a power system, a protection system scheme is needed to isolate the faulty part from the healthy part. If the protection system acts and a component is switched off, an outage in the power system occurs. A distinction can be made between forced outages (caused by failures of components switched off by the protection system, or disconnected manually by the system operator immediately) and planned outages (due to maintenance activities) [99]. Faults cause interruption to electric flows, equipment damages, and even death of humans and animals. The occurrence of a fault should be followed by the quick isolation of the faulty zone and the service restoration to those zones affected by the fault and to which the service can be reestablished. Only after reparation or replacement of the failed component, restoration can be then performed to reestablish the service to the entire system.

To clarify the scope of the paper, consider the following definitions [25]:

• fault detection is the task of recognizing the occurrence of a fault;
• fault classification is the identification of the fault type;
• fault isolation is the process of isolating the faulty part of the network after a successful detection;
• fault location is the task of localizing the fault (i.e. branch, zone, location point);
• service restoration is the process aimed at returning the system to normal operating conditions; that is, a task carried out to restore the service in healthy zones after detecting and isolating a permanent fault in the system.

In this paper, fault diagnosis is the combination of fault detection, classification and location. Neither isolation nor restoration are covered here. Methodologies and practices for service restoration depend on utilities and the voltage level of the affected system; see, for instance, [100,101].

As mentioned in the Introduction, faults in power systems can be classified into two groups: series and shunt. Figure 5 shows a more complete classification of faults in power systems; see, for instance, [21,34]. Faults can also be classified as temporary and permanent. Temporary faults are faults that occur for a certain period of time and can be cleared with or without interrupting operation (i.e. depending on their nature and duration, they can or cannot require the involvement of protective devices). Permanent faults will require the involvement of the protection system to interrupt the service within the faulty zone. A lightning stroke can cause a temporary fault in an overhead line without creating permanent damage; in addition, depending on the conditions within the impacted line, the fault can force or not the operation of the protection system. By default, a fault in an insulated cable is, irrespective of its cause, always permanent.

A fault in a distribution system is de-energized by opening and locking out breakers or reclosers. This lockout creates an outage for the faulted zone (i.e. customers downstream of the faulted zone, and also customers upstream of the faulted zone, in case of miscoordination). Distribution companies are implementing FLISR (fault location, isolation, and service restoration) solutions, also known as FDIR (fault detection, isolation, and restoration); see, for instance, [26,102,103]. FLISR automatically restores power to as many customers as possible, as quickly as possible. Without FLISR, an outage must be handled manually, often resulting in a longer outage larger. Deploying FLISR makes it possible to improve the coordination of switching devices and extend their utility into post-fault restoration; see Figure 6. FLISR technologies involve automated feeder switches and reclosers, line monitors, communication networks, supervisory control and data acquisition (SCADA) systems, remote terminal units, and data processing tools.

These technologies work in tandem to automate power restoration, reducing both the impact and length of power interruptions automatically as quickly as possible. In addition, the fault isolation feature can help crews locate the trouble location more quickly, resulting in shorter outage durations for the affected customers. A fault passage indicator (FPI) is a device used in distribution systems to detect and locate faults on power lines. When a fault occurs, the FPI detects the resulting change in current and transmits a signal to a central control system. When properly applied, FPIs can decrease operating costs and reduce service interruptions by quickly identifying the failed zone.

2.3. Scope of the Paper

Once the concepts to be used and analyzed in this paper have been defined, it is worth defining again the scope of this paper.

As mentioned in the previous subsections, a service interruption in a power system can be caused by a failure in many system components. However, many of these components are crucial for the operation of the system and they are well protected. Consequently, failures in any of these components will usually lead to the operation of its corresponding protection system, and locating the fault position should not be a serious issue. Of course, power systems are very complex and the location of the faulty component will not always be obvious. However, this task can be carried out, for instance, by an expert system developed for analyzing alarms. In any case, such location task would not be based on complex mathematical analyses. A different scenario is presented when the failure is on an overhead line or an insulated cable; the faulted position cannot be then always located by a fast visual inspection, mainly if it is inside an insulated and underground cable.

According to the concepts defined in the previous subsection a complete fault diagnosis might include the identification of the faulted section. Given the design of protective systems, this could be an issue for distribution systems for which the faulted section is not protected at any of its ends, and therefore the section is not isolated in case of fault. In such cases, the fault location and section identification must be carried out simultaneously.

The goal of this paper is to provide a bibliographical survey of AI-based techniques proposed to date for detection, classification and location of faults in overhead lines and insulated cables, at both transmission and distribution levels, being lines and cables operating in either AC or DC regimes.

4.1 . Introduction

The goals of this paper were presented in previous sections after providing a short introduction to the main topics and a brief description of the main concepts. This subsection presents a more detailed description of methods aimed at detecting, classifying and locating faults in lines and cables working at any voltage level, considering both AC and DC operation.

Figure 1 depicted a diagram of fault diagnosis, as assumed in this work. According to the figure the tasks to be performed to classify and locate a fault are as follows: (1) measurements of voltages and currents, (2) extraction of features from the measured signals; (3) fault detection; (4) fault classification; (5) fault location.

The techniques in fault diagnosis can be classified into two distinct categories [11]: model-based and process history-based; see Figure 9. Model-based methods use a mathematical model to describe the process system; the description is derived from the underlying physics of the process and can be quantitative or qualitative. Process history-based methods do not use an explicit analytical relationship to define the physical system, they use instead a large set of experimental data set from which they derive a function relating input and output. The relationship between inputs and outputs can be statistical (e.g., regression) or non-statistical (e.g., ANN, SVM).

In the rest of this section, it is assumed that voltage and current measurements are available to perform the subsequent tasks; for a review of sensor technologies used in fault diagnosis for power systems see reference [218]. A short introduction to those tasks is presented below.

Feature extraction: Although measured current and voltage signals contain all the required information for a fault diagnosis, it is not easy to apply a set of rules to sampled signals; a feature extraction is generally used to disclose the information required by the subsequent tasks. Detecting, classifying and locating a fault are much more easily performed by using an appropriate and reduced list of values derived from the sampled waveforms: dimensionally reduced data will boost the performance of any algorithm and provide faster results. The following classification of techniques aimed at extracting features from sampled signals was presented in reference [9].

• Transform methods: The frequency characteristics of current and voltage signals during a fault change with time, and they can be very useful for detecting, classifying and locating a fault. A variety of methods used to analyze frequency characteristics of time-domain signals have been proposed. Some of the most popular transform methods are the Fourier transform (FT), the wavelet transform (WT), and the S transform (ST). Since time-domain signals and frequency-domain coefficients are both discrete, in practice the most popular approaches use this condition for feature extraction; they are the discrete Fourier transform (DFT), the fast Fourier transform (FFT), or the discrete wavelet transform (DWT).
• Modal transformations: They transform/decouple three-phase quantities into components that can be used to further characterize fault types or to obtain detection and location indices. A list of popular transformations includes the Clark transformation, the Clarke-Concordia transformation, and the Karrenbauer transformation.
• Dimensionality reduction: This approach maps the data from the original high-dimensional space onto a low-dimensional subspace in which the variance of the data can be best accounted. The reduction is usually performed by means of principal component analysis (PCA), and can be combined with other methods.
• Other methods: To reduce the computational burden associated with the above listed methods, other approaches have been proposed. They are based on the RMS values of phase and zero sequence currents, the normalized ratios of maximum absolute values of currents for two different phases, or the ratios of phase angle differences between phases plus the ratio of zero sequence current amplitude to positive sequence current amplitude [9]. Mathematical morphology is another option that is being adopted as a feature extraction technique for detection and classification of faults [219]. For a comparison of feature extraction techniques, see this last reference.

Fault detection: This task is aimed at determining whether a fault exists or is developing. A thorough list of techniques for the detection of faults and failures in SGs is provided in reference [27].

The first obvious approach for detecting a fault in a line or a cable is based on a threshold value of a current parameter. Since high current values in power systems are associated with low voltage values, a more sophisticated approach can include a threshold value of a voltage parameter that would confirm the existence of a fault current. Although fault detection is generally performed using the information derived from the feature extraction, many modern algorithms are proficient enough to avoid this stage; for instance, when the classifier or the locator is capable of distinguishing between faulty and non-faulty states.

Detection methods independent of the classification methods use negative sequence components or parameters extracted by using a wavelet transform. Several methods have also been proposed for detecting high impedance faults using these approaches; see [9].

This task has been the subject of many review papers; see, for instance, references [131,143,153,190,192,197,220]. High impedance fault detection techniques on distribution networks have been reviewed in [44,45,221].

Fault classification:Figure 5 presented a classification of faults in power systems with two categories: series and shunt faults. Faults can also be static or evolving [222]. Evolving faults, also known as multi-stage faults, are those faults that begin as one type and change over time to another type; for instance, a single-line-to-ground fault that becomes a phase-to-phase with ground contact fault.

References [223] and [224] proposed to divide techniques applied to fault classification in power systems into the following three groups:

• Prominent techniques: They used one of the following three approaches for feature extraction: wavelet transform, ANNs, and fuzzy logic.
• Hybrid techniques: They are based on a combination of two or more approaches of the previous group (e.g., neuro-fuzzy technique, wavelet and ANN technique, wavelet and fuzzy-logic technique, wavelet and neuro-fuzzy technique).
• Modern techniques: They are based on other AI techniques (e.g., SVM, GA, PCA, or DT) or use modern technologies to either measure signals or process extracted signal features (FPGA-based implementation, PMU-based protection scheme, pilot scheme).

Although this classification was proposed for techniques applied to transmission levels, it can also be considered for techniques to be applied to distribution levels.

Reference [9] divides the techniques into methods based on logic flow and methods based on AI techniques. The techniques of the first group use a tree-like logic flow with multiple criterions and values derived from the application of some feature extraction. The techniques of the second group use fuzzy inference systems, ANNs, SVMs, DTs, and other ML techniques.

Fault location: Fault location is a process aimed at locating the fault with the highest possibly accuracy [1]. When locating faults on a line consists of more than one section (i.e., a three-terminal or multi-terminal line), the faulty section has to be identified and a fault in this section has to be located. Even the location of temporary faults is important since the fault location can help to pinpoint the weak spots on the line so maintenance plans can be fixed for avoiding future problems. In a few words, a fault location method estimates the physical location of a fault by processing the voltage and current waveforms or the extracted features from those waveforms.

A fault-location function can be implemented into microprocessor-based protective relays, digital fault recorders, stand-alone fault locators, and post-fault analysis programs [1,3]. A fault locator is a supplementary device with a fault-location algorithm for estimating the distance to the fault. Although fault locators and protective relays are closely related, there are some important differences between them: accuracy of fault location; speed of determining the fault position; speed of transmitting data from remote site; used data window; digital filtering of input signals and complexity of calculations [1]. Fault locators are used for accurately estimating the fault position and not only for indication of the general area where a fault occurred. Since protective relays perform their tasks online, high speed operation appears as a crucial requirement (i.e., the decision for tripping transmission lines has to be made in less than in one cycle of fundamental frequency). In contrast, the calculations of fault locators are performed in an off-line mode since the results of these calculations are for later use. This implies that the fault-location calculation can last seconds or even minutes. That is, the required high speed of protective relays imposes not too complex and too time consuming calculations; in contrast, fault-location calculations do not have such limitations. Consequently, the models of the power line and the fault in fault-location algorithms are usually more advanced than for relaying [1].

Distance relays are the protective relays more closely related to fault locators: they are designed for fast and reliable indication of the general area where a fault occurred. Generally, a pair of distance relays is used to protect a two-terminal line. Usually, they can communicate with each other, forming a pilot relay. The operation of a distance relay may be significantly influenced by the combined effect of load and fault resistance. The distance relay may not operate if the value of the fault resistance is too large. The value of the fault resistance may be particularly large for ground faults, which are the most frequent faults on overhead lines.

Methods based on visual inspection do not satisfy the requirements imposed on modern grids on fault location (i.e., automatic and fast location of the fault). The most popular fault location methods can be classified into the following categories [1]:

• Techniques based on fundamental-frequency currents and voltages: These techniques assume that the calculated impedance of the faulted-line segment is a measure of the distance to fault. When applied to a two-terminal line, they can be classified considering the available measurements: waveforms from one or both ends; complete or incomplete measurements (voltage or current) from a particular line end. Methods using one-end impedance techniques do not need communication means and their implementation into digital protective relays or digital fault recorders is rather simple. However, the algorithms will be more accurate if information from the two line terminals is available; therefore, if communication channels are at the disposal, then two-terminal fault-location methods should be used since low-speed communications are sufficient. Besides, two-end techniques exhibit more accuracy without any assumption about external networks (i.e., impedances of the equivalent sources).
• Techniques based on traveling-wave phenomenon: These techniques use voltage and current waves, traveling at the speed of light from the fault towards the line terminals; they can be very accurate, but also complex and expensive due to the required high sampling frequency.
• Techniques based on high-frequency components of currents and voltages: These techniques are also complex and expensive since they require specially tuned filters for measuring high-frequency components.
• AI-based techniques: Although ANN-based methods for fault location have been developed for more than forty years, it has been during the last two decades when a significant effort has been dedicated to fault-location techniques both in transmission and distribution networks using AI methods. A high number of review papers focused on this subject has been published during this period; see references [8,9,14,15,16,22,24,25,30,33,34,36,225,226].

Two interesting guides on fault location methods in AC power systems are available in the technical brochure written by the CIGRE WG B5.52 [227] and in the IEEE standard C37.114-2014 [228].

Table 3 shows a selected list of review papers on fault diagnosis published since 2021.

The reminder of this section is dedicated to reviewing AI-based fault diagnosis techniques applied to power systems. The next two subsections are respectively dedicated to reviewing the work made on fault diagnosis of both transmission and distribution systems. The last subsection is dedicated to review works focused on DC systems.

4.2. Fault Location Methods for Transmission Systems

References [229,230,231,232,233,234,235,236,237,238,239,240,241,242,243,244,245,246,247,248,249,250,251,252,253,254,255,256,257,258,259,260,261,262,263,264,265,266,267,268,269,270,271,272,273,274,275,276,277,278,279,280,281,282,283,284,285,286,287,288,289,290,291,292,293,294,295,296,297,298,299,300,301,302,303,304,305,306,307,308,309,310,311,312,313,314,315,316,317,318,319,320,321,322,323,324,325,326] provide a list of papers that deal with AI-based techniques applied to detection, classification and location of faults in transmission systems, ordered by publication date. Note that, as for other power system studies and applications, AI-based techniques has been an option since 1980s. Due to room limitations, only a selection of those published since 2021 is presented in Table 4. Although the table does not provide too much information about the selected works, there are several conclusions that can be drawn:

• There is a high number of AI techniques that can be useful to faults diagnosis of power systems. Remember that the topics covered in this survey are focused on faults/failures that can affect overhead lines and insulated cables only.
• None of the selected papers deals with automatic AI-based fault diagnosis of transmission-level insulated cables.
• Only a small percentage of papers deal with a complete fault diagnosis procedure (i.e., detection, classification and location). Actually, the fault location seems to be the task to which a lower number of papers has been dedicated.
• Supervised ML techniques (i.e., NN, SVM, DT) are the most popular group of AI applications.

A high percentage of the selected works is based on either a two-terminal transmission line model or a rather small benchmark system, usually an IEEE test system. Only a few works used public datasets (e.g. datasets available in Kaggle); see, for instance, references [323] and [326].

Table 3. – Selected reviews on fault diagnosis in power systems using AI-based techniques.

Ref.	Authors	Year	Topics
[37]	Daang et al.	2024	Fault detection in transmission lines
[36]	Kanwal and Jiriwibhakorn	2024	Fault detection, classification, and location in transmission lines
[35]	Shukla and Deepa	2024	Fault classification in transmission lines
[33]	Liu et al.	2023	Fault location in transmission lines
[32]	Shakiba et al.	2023	Fault detection, classification, and location in transmission lines
[31]	Jena et al.	2023	Fault detection, classification, and location in underground cables
[30]	Rezapour et al.	2023	Fault location in distribution grids
[8]	De La Cruz et al.	2023	Fault location in smart distribution grids and MGs
[43]	Baharozu et al.	2023	High impedance fault location
[28]	Shafiullah et al.	2022	Comparison of ML techniques for distribution grid fault analysis
[26]	Srivastava et al.	2022	Fault detection, isolation, and restoration in distribution grids
[25]	Stefanidou-Voziki et al.	2022	Fault classification and location in distribution grids
[24]	Dashti et al.	2021	Fault prediction and location in smart distribution grids and MGs
[23]	Vaish et al.	2021	Fault detection, isolation, and restoration in power systems
[22]	Mukherjee et al.	2021	Fault detection, classification, and location in transmission lines

MG: Microgrid, ML: Machine Learning.

Table 3. – Selected reviews on fault diagnosis in power systems using AI-based techniques.

Ref.	Authors	Year	Topics
[37]	Daang et al.	2024	Fault detection in transmission lines
[36]	Kanwal and Jiriwibhakorn	2024	Fault detection, classification, and location in transmission lines
[35]	Shukla and Deepa	2024	Fault classification in transmission lines
[33]	Liu et al.	2023	Fault location in transmission lines
[32]	Shakiba et al.	2023	Fault detection, classification, and location in transmission lines
[31]	Jena et al.	2023	Fault detection, classification, and location in underground cables
[30]	Rezapour et al.	2023	Fault location in distribution grids
[8]	De La Cruz et al.	2023	Fault location in smart distribution grids and MGs
[43]	Baharozu et al.	2023	High impedance fault location
[28]	Shafiullah et al.	2022	Comparison of ML techniques for distribution grid fault analysis
[26]	Srivastava et al.	2022	Fault detection, isolation, and restoration in distribution grids
[25]	Stefanidou-Voziki et al.	2022	Fault classification and location in distribution grids
[24]	Dashti et al.	2021	Fault prediction and location in smart distribution grids and MGs
[23]	Vaish et al.	2021	Fault detection, isolation, and restoration in power systems
[22]	Mukherjee et al.	2021	Fault detection, classification, and location in transmission lines

MG: Microgrid, ML: Machine Learning.

As for software tools, most of the simulations required to obtain data were carried out using MATLAB/Simulink or an EMTP-like tool (e.g., ATP, PSCAD). This allowed authors to implement highly accurate models, although not all of them did choose such an option (e.g., a frequency-dependent model for representing overhead lines).

Additional works related to fault diagnosis of transmission-level systems in which new and emerging technologies (e.g., quantum computing, unmanned aerial vehicles-UAV) were adopted are mentioned in Section V.

4.3. Fault Location Methods for Distribution Systems

References [327,328,329,330,331,332,333,334,335,336,337,338,339,340,341,342,343,344,345,346,347,348,349,350,351,352,353,354,355,356,357,358,359,360,361,362,363,364,365,366,367,368,369,370,371,372,373,374,375,376,377,378,379,380,381,382,383,384,385,386,387,388,389,390,391,392,393,394,395,396,397,398,399,400,401,402,403,404,405,406,407,408,409,410,411,412,413,414,415,416,417,418,419,420,421,422,423,424,425,426,427,428,429,430,431,432,433,434] provide a list of papers dealing with AI-based techniques applied to detection, classification and location of faults in distribution systems. As for papers dealing with fault diagnosis of transmission systems, they are ordered by publication date.

Again, only a selection of those published since 2021 is presented in Table 5, from which some conclusions be drawn:

• As for research related to transmission systems, a high number of AI techniques has also been applied to faults diagnosis of distribution systems.
• Only a small percentage of works deals with a complete fault diagnosis procedure (i.e., detection, classification and location).
• Supervised ML techniques are again the most popular option for authors interested in this field.

A high percentage of the selected papers is based on either a two-terminal line model or a rather small system. Although IEEE test systems were the option selected by many authors, many others used custom-made distribution system models. As for software tools, most of the simulations were carried out again using MATLAB/Simulink or an EMTP-like tool (e.g., ATP, PSCAD).

Table 4. – Selected papers on AI-based fault diagnosis in transmission systems.

Ref.	Authors	Year	Task	Technique
[326]	Anwar et al.	2025	Fault detection	RF, LSTM, and kNN
[325]	Nayak et al.	2024	Fault detection and classification	CWT and 2D-CNN
[324]	Wu et al.	2024	Fault classification and location	PNMCN
[323]	Turanli and Yakut	2024	Fault classification	1D-CNN
[322]	Jia et al	2024	Fault location	CEEMDAN, MSA, and ConvGRU
[321]	de Alencar et al.	2024	Fault classification	ICA and CNN
[320]	Alhanaf et al.	2024	Fault detection and classification	Hybrid CNN-LSTM
[319]	Najafzadeh et al.	2024	Fault detection, classification, and location	WHO-RF/DT, ANFIS
[318]	Ukwuoma et al.	2024	Fault detection, classification, and location	MSAN, DGNN, and MLP
[317]	Chen and Liu	2024	Fault detection	FuzREANN
[316]	Mampilly and Sheeba	2023	Fault detection and classification	WT and ICNN-BOA
[315]	Bhattacharya and Nigam	2023	Fault detection and classification	RF, DT, XGB, LGBM
[314]	Altaie et al.	2023	Fault detection	Several ML techniques
[313]	Alhanaf et al.	2023	Fault detection, classification, and location	ANN, DNN
[312]	Khan et al.	2023	Fault classification and location	VAE and SVM, kNN, RF, DT
[311]	Biswas et al.	2023	Fault detection and classification	VMD and CNN
[310]	Zhang and Wang	2023	Fault classification	GLDA-CE
[308]	Goni et al.	2023	Fault detection and classification	ELM
[307]	Sahoo and Samal	2023	Fault detection and classification	DNN
[306]	Thomas et al.	2023	Fault detection and location	CNN
[305]	Rajesh et al.	2022	Fault detection and classification	TSVD-HUA-RPNN
[304]	Fahim et al.	2022	Fault detection and classification	DBN
[303]	Hong et al.	2022	Fault classification and location	CNN
[302]	França et al.	2022	Fault classification	MLPN, RBF, SVM, DT
[300]	Gutierrez-Rojas et al.	2022	Fault classification	DM-DFT and QARMA
[298]	Arranz et al.	2021	Fault location	ST and ANN
[297]	Fahim et al.	2021	Fault detection and classification	WT and CNSF
[296]	Rafique et al.	2021	Fault detection and classification	e2e learning and LSTM
[295]	Mukherjee et al	2021	Fault detection and location	PCA
[294]	Hassani et al.	2021	Fault classification	kGAN and kNN, SVM
[293]	Mukherjee et al.	2021	Fault classification	PCA
[292]	Belagoune et al.	2021	Fault detection, classification, and location	DRNN-LSTM
[291]	Srikanth and Koley	2021	Fault classification	ST and 3D CNN
[290]	Haq et al.	2021	Fault detection and classification	DWT and ELM
[289]	Vyasa et al.	2021	Fault detection and classification	DWT and ChNN
[288]	Han et al.	2021	Fault classification	GSV-CDA-CNN

Note: The acronyms used in this table are defined after the Introduction of this paper.

Table 4. – Selected papers on AI-based fault diagnosis in transmission systems.

Ref.	Authors	Year	Task	Technique
[326]	Anwar et al.	2025	Fault detection	RF, LSTM, and kNN
[325]	Nayak et al.	2024	Fault detection and classification	CWT and 2D-CNN
[324]	Wu et al.	2024	Fault classification and location	PNMCN
[323]	Turanli and Yakut	2024	Fault classification	1D-CNN
[322]	Jia et al	2024	Fault location	CEEMDAN, MSA, and ConvGRU
[321]	de Alencar et al.	2024	Fault classification	ICA and CNN
[320]	Alhanaf et al.	2024	Fault detection and classification	Hybrid CNN-LSTM
[319]	Najafzadeh et al.	2024	Fault detection, classification, and location	WHO-RF/DT, ANFIS
[318]	Ukwuoma et al.	2024	Fault detection, classification, and location	MSAN, DGNN, and MLP
[317]	Chen and Liu	2024	Fault detection	FuzREANN
[316]	Mampilly and Sheeba	2023	Fault detection and classification	WT and ICNN-BOA
[315]	Bhattacharya and Nigam	2023	Fault detection and classification	RF, DT, XGB, LGBM
[314]	Altaie et al.	2023	Fault detection	Several ML techniques
[313]	Alhanaf et al.	2023	Fault detection, classification, and location	ANN, DNN
[312]	Khan et al.	2023	Fault classification and location	VAE and SVM, kNN, RF, DT
[311]	Biswas et al.	2023	Fault detection and classification	VMD and CNN
[310]	Zhang and Wang	2023	Fault classification	GLDA-CE
[308]	Goni et al.	2023	Fault detection and classification	ELM
[307]	Sahoo and Samal	2023	Fault detection and classification	DNN
[306]	Thomas et al.	2023	Fault detection and location	CNN
[305]	Rajesh et al.	2022	Fault detection and classification	TSVD-HUA-RPNN
[304]	Fahim et al.	2022	Fault detection and classification	DBN
[303]	Hong et al.	2022	Fault classification and location	CNN
[302]	França et al.	2022	Fault classification	MLPN, RBF, SVM, DT
[300]	Gutierrez-Rojas et al.	2022	Fault classification	DM-DFT and QARMA
[298]	Arranz et al.	2021	Fault location	ST and ANN
[297]	Fahim et al.	2021	Fault detection and classification	WT and CNSF
[296]	Rafique et al.	2021	Fault detection and classification	e2e learning and LSTM
[295]	Mukherjee et al	2021	Fault detection and location	PCA
[294]	Hassani et al.	2021	Fault classification	kGAN and kNN, SVM
[293]	Mukherjee et al.	2021	Fault classification	PCA
[292]	Belagoune et al.	2021	Fault detection, classification, and location	DRNN-LSTM
[291]	Srikanth and Koley	2021	Fault classification	ST and 3D CNN
[290]	Haq et al.	2021	Fault detection and classification	DWT and ELM
[289]	Vyasa et al.	2021	Fault detection and classification	DWT and ChNN
[288]	Han et al.	2021	Fault classification	GSV-CDA-CNN

Note: The acronyms used in this table are defined after the Introduction of this paper.

Table 5. – Selected papers on AI-based fault diagnosis in distribution systems.

Ref.	Authors	Year	Task	Technique
[433]	Shafei et al.	2024	Fault detection, classification and location	PT-PFPT and CNN
[432]	Arsoniadis and Nikolaidis	2024	Fault location	WSN and SVM/ERM
[431]	Barkhi et al.	2024	Fault detection and classification in MGs	SVM
[430]	Krishnamurthy et al.	2024	Fault classification	RF
[429]	Yildiz and Abur	2024	Fault detection and location	CNN
[428]	Fan et al.	2024	Fault location	VGAE-GraphSAGE
[427]	Basher et al.	2024	Fault classification and location in MGs	DWT with DTE-LDA
[426]	Bhagwat et al.	2024	Fault detection, classification and location	Customised ANN
[425]	Awasthi et al.	2024	Fault classification	kNN
[424]	Liang et al.	2024	Fault location	Multi-head GAT
[423]	Zhou et al.	2024	Fault classification and location	CNN
[422]	Cieslak et al.	2024	HIF classification in MGs	TNN
[421]	Li	2024	HIF detection and location	CNN-LSTM
[419]	Mampilly and Sheeba	2024	Fault detection and classification in MGs	EWT-HCRNN-POA
[418]	Awasthi et al.	2024	Fault classification	Shallow ANN
[417]	Bhatnagar et al.	2024	Fault detection and classification	CNN-LSTM-AM
[416]	Mbey et al.	2023	Fault detection and classification	LSTM-ANFIS
[415]	Mirshekali et al.	2023	Fault location	Spectrogram and CNN-CN
[408]	Yang and Yang	2023	Fault classification	STFT and CNN-FDTW
[407]	Kurup et al.	2023	Fault detection and classification	CNN and SVM
[406]	Mo et al.	2023	Fault classification and location	Super-resolution and GNN
[405]	Haydaroğlu and Gümüş	2023	Fault detection	Cauchy-M and RVFLN
[404]	Rizeakos et al.	2023	Fault classification and location	CWT and CNN
[403]	Yuan and Jiao	2023	Fault detection	Hybrid CNN-LSTM
[398]	Dashtdar et al.	2023	Fault location	GA
[397]	Hu et al.	2023	Fault classification and location	STGCN
[396]	da Silva Santos et al.	2022	Fault detection and classification	DWT and FIS
[395]	Moloi et al.	2022	Fault classification and location	WPD and SVM
[394]	Ahmadipour et al.	2022	Fault detection and classification in MGs	MODWPT-ALPSO-SVM
[393]	Granado Fornás et al.	2022	Fault detection and classification	TDR, GAF, and GAN
[389]	Rai et al.	2022	HIF detection and classification	TNN-CNN
[385]	Carvalho et al.	2022	HIF classification	HOS-FDR with ANN
[383]	Mirshekali et al.	2022	Fault location	NCFS and SVM
[382]	Swaminathan et al.	2021	Fault classification and location in cables	CNN-LSTM
[381]	de Freitas and Coelho	2021	Fault location	GNN
[380]	Gilanifar et al.	2021	Fault classification	MTLS-LR
[378]	Yu et al.	2021	Fault location	SIG-CNN
[376]	Okumus and Nuroglu	2021	Fault location	WT-FWHT and RF
[374]	Baloch and Muhammad	2021	Fault detection and classification in MGs	HT, LR and AB

Note: The acronyms used in this table are defined after the Introduction of this paper.

Table 5. – Selected papers on AI-based fault diagnosis in distribution systems.

Ref.	Authors	Year	Task	Technique
[433]	Shafei et al.	2024	Fault detection, classification and location	PT-PFPT and CNN
[432]	Arsoniadis and Nikolaidis	2024	Fault location	WSN and SVM/ERM
[431]	Barkhi et al.	2024	Fault detection and classification in MGs	SVM
[430]	Krishnamurthy et al.	2024	Fault classification	RF
[429]	Yildiz and Abur	2024	Fault detection and location	CNN
[428]	Fan et al.	2024	Fault location	VGAE-GraphSAGE
[427]	Basher et al.	2024	Fault classification and location in MGs	DWT with DTE-LDA
[426]	Bhagwat et al.	2024	Fault detection, classification and location	Customised ANN
[425]	Awasthi et al.	2024	Fault classification	kNN
[424]	Liang et al.	2024	Fault location	Multi-head GAT
[423]	Zhou et al.	2024	Fault classification and location	CNN
[422]	Cieslak et al.	2024	HIF classification in MGs	TNN
[421]	Li	2024	HIF detection and location	CNN-LSTM
[419]	Mampilly and Sheeba	2024	Fault detection and classification in MGs	EWT-HCRNN-POA
[418]	Awasthi et al.	2024	Fault classification	Shallow ANN
[417]	Bhatnagar et al.	2024	Fault detection and classification	CNN-LSTM-AM
[416]	Mbey et al.	2023	Fault detection and classification	LSTM-ANFIS
[415]	Mirshekali et al.	2023	Fault location	Spectrogram and CNN-CN
[408]	Yang and Yang	2023	Fault classification	STFT and CNN-FDTW
[407]	Kurup et al.	2023	Fault detection and classification	CNN and SVM
[406]	Mo et al.	2023	Fault classification and location	Super-resolution and GNN
[405]	Haydaroğlu and Gümüş	2023	Fault detection	Cauchy-M and RVFLN
[404]	Rizeakos et al.	2023	Fault classification and location	CWT and CNN
[403]	Yuan and Jiao	2023	Fault detection	Hybrid CNN-LSTM
[398]	Dashtdar et al.	2023	Fault location	GA
[397]	Hu et al.	2023	Fault classification and location	STGCN
[396]	da Silva Santos et al.	2022	Fault detection and classification	DWT and FIS
[395]	Moloi et al.	2022	Fault classification and location	WPD and SVM
[394]	Ahmadipour et al.	2022	Fault detection and classification in MGs	MODWPT-ALPSO-SVM
[393]	Granado Fornás et al.	2022	Fault detection and classification	TDR, GAF, and GAN
[389]	Rai et al.	2022	HIF detection and classification	TNN-CNN
[385]	Carvalho et al.	2022	HIF classification	HOS-FDR with ANN
[383]	Mirshekali et al.	2022	Fault location	NCFS and SVM
[382]	Swaminathan et al.	2021	Fault classification and location in cables	CNN-LSTM
[381]	de Freitas and Coelho	2021	Fault location	GNN
[380]	Gilanifar et al.	2021	Fault classification	MTLS-LR
[378]	Yu et al.	2021	Fault location	SIG-CNN
[376]	Okumus and Nuroglu	2021	Fault location	WT-FWHT and RF
[374]	Baloch and Muhammad	2021	Fault detection and classification in MGs	HT, LR and AB

Note: The acronyms used in this table are defined after the Introduction of this paper.

An interesting aspect has been the option selected by authors to perform the feature extraction task. Wavelet transform has been a popular approach but many works were based on different approaches, such as CAE, SIG or STFT (see Table 5).

4.4. Fault Location Methods for DC Systems

Although there are significant differences between the technologies currently implemented to protect DC lines and cables with respect to those implemented for protecting AC systems, fault diagnosis techniques applied to both types of systems exhibit some similarities. The protection of DC systems has been the subject of several recent review papers [435,436,437,438]. Likewise, fault detection, identification and location techniques applied to DC systems have also been reviewed in some recent works [438,439,440,441]. Reference [438] provides an extensive review of techniques implemented for protection and fault diagnosis of HVDC systems, including the technologies used in their design. As with AC systems, a significant effort has been made during the last decades for analyzing the potential that AI-based techniques offer in fault diagnosis of DC systems; see [442,443,444,445,446,447,448,449,450,451,452,453,454,455,456,457,458,459,460,461,462,463,464,465,466,467,468,469,470,471,472,473]. Table 6 provides a selection of papers published since 2021. Note that the list of references mentioned in the table includes papers related to any voltage level and covers the most common technologies (i.e., CSC, VSC, MMC). The test system configurations (an aspect not mentioned in the table) cover from two-terminals [442,443,458,462,471,473] to multi-terminal DC systems [443,446,457,461,468,469], as well as low-voltage DC microgrids [450,465,466] and systems based on the CIGRE benchmark model [442]. As for the techniques, there are examples from all AI subfields, but it seems that those based on neural networks are the most popular.