Survey on Methods for Detection, Classification and Location of Faults in Power Systems Using Artificial Intelligence

Alexandre Serrano-Fontova; Juan A. Martinez-Velasco; Pau Casals-Torrens; Ricard Bosch-Tous

REVIEW 2 major objections 4 minor 296 references

Survey on Methods for Detection, Classification and Location of Faults in Power Systems Using Artificial Intelligence

T0 review · 2 major / 4 minor · reviewed 2026-08-06 · deepseek-v4-flash

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Pith's one-line read Supervised machine learning dominates AI fault diagnosis in power systems, and full detection-to-location pipelines remain rare in the surveyed literature.

desk verdict A useful, broad survey whose descriptive conclusions are plausible but rest on an undocumented reference selection the authors themselves flag as debatable. read the letter →

arxiv 2507.10011 v1 pith:JMA2BXML submitted 2025-07-14 eess.SY cs.SY

Juan A. Martinez-Velasco , Alexandre Serrano-Fontova , Ricard Bosch-Tous , Pau Casals-Torrens This is my paper · ORCID

classification eess.SYcs.SY

keywords artificialintelligencefaultdetectionclassificationlocationmachinelearningdeeppowersystemprotectiondistributedenergyresources

verification ladder T0 review T1 audit T2 compute T3 formal

The pith

A machine-rendered reading of the paper's core claim, the machinery that carries it, and where it could break.

The reading

This survey consolidates recent work on using artificial intelligence to detect, classify, and locate faults on power-system lines and cables, across transmission and distribution levels, in AC and DC systems, with or without distributed energy resources. The paper's central observations are that supervised machine-learning techniques — neural networks, support vector machines, decision trees — are the most popular family, that only a minority of studies cover the full detection-classification-location chain, and that none of the surveyed papers applies automatic AI-based fault diagnosis to transmission-level insulated cables. It also finds that most studies validate on small two-terminal or benchmark models using simulated data rather than field measurements. The practical motivation is that faster, more accurate fault diagnosis shortens outages and protects equipment as grids become more complex and bidirectional.

What carries the argument

The organizing mechanism of the survey is the fault-diagnosis pipeline: measurements of voltages and currents, feature extraction (transforms such as the wavelet and Fourier transforms, modal transformations, and dimensionality reduction), then the three diagnosis stages of detection, classification, and location of the fault. The paper uses this pipeline as its classification scheme, together with a taxonomy of AI families (machine learning, metaheuristics, rule-based expert systems, and deep learning as a subset of machine learning), to sort and compare the methods found in the literature.

What would settle it

A systematic literature search with explicit inclusion criteria covering the same time window and databases would settle the claim: if it finds multiple papers that apply automatic AI-based fault diagnosis to transmission-level insulated cables, or finds unsupervised or reinforcement learning used as often as supervised learning, the survey's main gap and dominance conclusions would be contradicted.

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Extended reading notes

Core claim

The paper claims that the AI-based fault-diagnosis literature for lines and cables is organized around a three-stage pipeline — detection, classification, location — fed by feature extraction from voltage and current measurements, and that within this pipeline supervised machine learning is the dominant approach at both transmission and distribution levels and in DC systems. The fault-location stage receives the least attention in the selected papers, and no selected paper performs automatic AI-based fault diagnosis of transmission-level insulated cables. The paper further claims that most reported methods are trained and tested on simulated data from two-terminal line models or small benchmark systems, usually generated with MATLAB/Simulink or EMTP-like tools, which limits how directly their reported performance transfers to real grids.

Load-bearing premise

The survey's conclusions assume that the manually selected, non-exhaustive set of references fairly represents the broader literature on AI-based fault diagnosis; the paper itself acknowledges in its discussion that the selection is unavoidable and debatable, and no systematic search or inclusion criteria are provided.

Editorial extensions

If this is right

New work on fault location in lines and cables addresses the least-covered stage of the diagnosis pipeline, where the survey's selected papers are scarcest.
Automatic AI-based fault diagnosis of transmission-level insulated cables is an open niche that the survey's sample does not fill.
Reported accuracies from small simulated benchmark models may not transfer to real grids, so validation on larger models with instrument transformers and field data is a direct next step.
Supervised machine learning is the default choice in the selected literature; unsupervised, reinforcement, and hybrid approaches are comparatively rare.
For distribution systems with distributed generation and microgrids, AI-based diagnosis is being adapted to bidirectional power flow, which changes the protection and fault-location problem.

Reading between the lines

Editorial extensions of the paper, not claims the author makes directly.

A reproducible systematic review with explicit inclusion criteria could test whether the 'no transmission-level cable diagnosis' gap is a genuine gap in the field or an artifact of the paper's selection.
Because most training data are simulated, benchmarking the same algorithms on publicly available field fault records could reveal how much of the reported accuracy is simulation-specific.
The observed dominance of supervised learning suggests explainability, data efficiency, and decentralized training (e.g., federated learning) as the likely differentiators for the next generation of fault-diagnosis systems.
Hybrid schemes combining expert systems with machine learning, which the paper mentions as a practical option, may be the fastest route to deployment in grids with limited monitoring.

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Editorial analysis

A structured set of objections, weighed in public.

Desk editor's note, referee report, and a circularity audit.

Desk Editor's Note

read the letter

This is a broad but methodologically soft survey. You should know two things: it is a genuinely useful map of AI fault-diagnosis work across AC/DC and transmission/distribution, and its central descriptive claims (supervised ML dominates, full pipelines are rare) are plausible but rest on an unverified reference selection. The authors themselves admit the selection is 'unavoidable' and 'debatable' in Section V, so the candor is there, but the claims are still not checkable.

What the paper does well: the taxonomy is clear, the definitions of fault/failure and the three-stage diagnosis pipeline are helpful, and the tables give a quick way to see which techniques are used for which task. The decision to scope the review to lines and cables, and to include DC systems and microgrids, makes it more comprehensive than most surveys in this space. The specific point about transmission-level insulated cables is properly scoped to 'none of the selected papers,' which is the right way to put it. The bibliography is large and current.

Where it is soft: there is no systematic selection protocol—no database, query, or screening criteria—so the observed frequencies of techniques are properties of the sample, not necessarily of the field. The popularity claims are written as field statements even though they are sample statements. This is a real weakness, but it is not a fatal one. For a survey, the lack of quantitative comparison is also a limitation, but the paper positions itself as a descriptive overview, which is fine for the audience. Minor issue: tables only include papers since 2021, while the text references older work; this is okay but should be stated more explicitly.

Who it's for: newcomers and utility engineers who want a quick scan of AI techniques per task and voltage level. It is not for someone needing a systematic or quantitative review.

It deserves a serious referee. The scope and reference list justify the time. The main revision request would be transparency: a short paragraph on how papers were selected, or a scoping statement that conclusions apply to the selected sample. I would accept it with that change.

Referee Report

2 major / 4 minor

Summary. This paper presents a survey of AI-based techniques for fault detection, classification, and location in power systems, covering overhead lines and insulated cables at transmission and distribution levels, including AC and DC systems and microgrids. The paper defines AI and fault-diagnosis concepts, briefly reviews AI applications in power systems, and devotes its core section to classifying and summarizing selected recent works (mostly published since 2021) in tables grouped by transmission, distribution, and DC systems. The paper concludes that supervised ML techniques are the most popular group, that only a small fraction of selected works implement a complete detection-classification-location pipeline, and that neural networks dominate DC fault-diagnosis work. A discussion section addresses emerging topics such as generative AI, explainable AI, federated learning, UAV-based inspection, quantum computing, and cybersecurity, and the authors explicitly acknowledge in Section V that the selection of references is 'unavoidable' and 'debatable.'

Significance. If the selection limitations are addressed, this manuscript could serve as a useful, clearly written entry point to the large and fragmented literature on AI-based fault diagnosis. Its strengths are its broad scope (transmission, distribution, AC, DC, microgrids, and lines versus cables), the consistent definition of fault-diagnosis concepts, the curated tables that organize recent work by task and technique, and the balanced discussion of emerging topics such as XAI and federated learning. The paper is not a systematic review and does not provide quantitative performance comparisons, but such comparisons are not promised; the main contribution is a descriptive taxonomy and a selected bibliography. The value of that contribution depends on the representativeness of the manually assembled reference set, which is currently not verifiable.

major comments (2)

[Section 4.2, Section 4.3, Section V item 5] The paper's central descriptive conclusions—e.g., 'Supervised ML techniques (i.e., NN, SVM, DT) are the most popular group of AI applications' (Section 4.2, bullet 4) and 'Only a small percentage of works deals with a complete fault diagnosis procedure' (Sections 4.2 and 4.3)—are stated as properties of the literature, but they are derived from a manually selected set of references with no documented search strategy, database, query string, screening procedure, or eligibility criteria. The authors themselves concede in Section V item 5 that 'some selection has been unavoidable' and that 'it is debatable the way in which the papers included in this survey have been selected.' Because the observed frequencies could be an artifact of which venues, years, and topics were preferentially included, the main survey-level claims are not currently checkable. I request that the authors either (a) add a methodology subsection that specifies the databases, search terms, inclusion/exclusion criteria, and screening process, or (b) explicitly re-scope every such conclusion to 'in the selected sample' throughout the text, abstract, and conclusions. Without this change, the load-bearing assumption of representativeness remains unsupported.
[Section 4.4, paragraph after Table 6] The conclusion that 'it seems that those based on neural networks are the most popular' in DC fault diagnosis rests on a hand-selected table of only 20 references (Table 6), with no indication of how those references were chosen from the larger set [459-490] or from the broader DC literature. The hedge 'it seems' is appropriate, but the claim still implies a statement about the field rather than the sample. The same re-scoping or methodological fix requested for Sections 4.2 and 4.3 should be applied here, so that the reader can distinguish field-level popularity from sample-level frequency.

minor comments (4)

[Abstract] The abstract contains minor grammatical slips: 'results in expensive repair costs' should be 'result in expensive repair costs,' and 'at anytime' should be 'at any time.' A careful proofread would improve readability.
[Section 4.2 and 4.3, tables and text ranges] The text refers to continuous reference ranges such as [343-451] for distribution-system papers, but the corresponding tables list only a 'selection' and skip many numbers (e.g., Table 4 does not include [325], [317], or [315]; the range [343-451] suggests that some entries, including [451], may be missing from Table 5). Please verify that every reference cited in the text or tables appears in the reference list and that the stated ranges accurately describe the full set of related references rather than the selected subset.
[Section 2.3] The scope of the paper is defined three times (Introduction, Section 2.3, and again at the start of Section IV). While some repetition is useful in a survey, the third repetition could be shortened to a single sentence pointing to Section 2.3.
[Section V, item 3] The paper notes that game theory and multi-agent systems are not considered AI subfields in this work, but this exclusion is only mentioned in the discussion. For clarity, it would be helpful to state this scope decision earlier, alongside the definitions in Section 2.1.

Circularity Check

0 steps flagged · score 0.0 of 10

No circularity: the survey's descriptive conclusions rest on a manually selected reference set, a limitation the authors explicitly concede, not on any fitting, self-citation chain, or definitionally forced result.

full rationale

This is a bibliographic survey. Its claims (supervised ML is the most popular category; few selected papers perform complete fault diagnosis; neural networks dominate DC work) are frequency statements about a manually selected list of external papers [243-342], [343-451], [459-490]. No parameter is fitted and no quantity is predicted: the 'conclusions' in Sections 4.2-4.4 are direct summaries of table entries and cited works. The authors explicitly state in Section V.5 that 'some selection has been unavoidable' and 'it is debatable the way in which the papers included in this survey have been selected', converting the representativeness limitation into a declared caveat rather than a hidden input. Section 2.2 contains a self-referential pair of definitions (failure may lead to fault, fault may lead to failure), but the text immediately labels the pair circular and uses the distinction only to fix terminology; it does not feed any result. No self-citation is load-bearing: the reference list is dominated by external works, and no uniqueness theorem or prior result by the same authors is invoked. The survey is therefore self-contained as a descriptive review; any concern about reference-selection bias is a validity limitation, not circular reasoning.

Assumptions & free parameters 0 free parameters · 2 assumptions · 0 invented entities

The paper contributes no new mathematical objects; its load-bearing assumptions are about literature selection and taxonomy.

assumptions (2)

ad hoc to paper The non-exhaustive, hand-selected set of references is representative of the AI fault diagnosis literature.
Section V, item 5: the authors acknowledge the selection is 'unavoidable' and 'debatable'; without a systematic protocol, representativeness is assumed.
domain assumption The classification of fault diagnosis into detection, classification, and location, and the grouping of AI into ML, metaheuristics, and rule-based systems, is adequate for organizing the reviewed literature.
Section II defines these categories; the survey's tables and conclusions depend on this taxonomy.

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Cite this review

Pith. "Pith review of Survey on Methods for Detection, Classification and Location of Faults in Power Systems Using Artificial Intelligence." pith.science (2026). https://pith.science/paper/JMA2BXML

@misc{pith2026250710011,
  author       = {Pith},
  title        = {Pith review of: Survey on Methods for Detection, Classification and Location of Faults in Power Systems Using Artificial Intelligence},
  year         = {2026},
  howpublished = {\url{https://pith.science/paper/JMA2BXML}},
  note         = {Machine review of arXiv:2507.10011}
}

read the original abstract

Components of electrical power systems are susceptible to failures caused by lightning strikes, aging or human errors. These faults can cause equipment damage, affect system reliability, and results in expensive repair costs. As electric power systems are becoming more complex, traditional protection methods face limitations and shortcomings. Faults in power systems can occur at anytime and anywhere, can be caused by a natural disaster or an accident, and their occurrence can be hardly predicted or avoided; therefore, it is crucial to accurately estimate the fault location and quickly restore service. The development of methods capable of accurately detecting, locating and removing faults is essential (i.e. fast isolation of faults is necessary to maintain the system stability at transmission levels; accurate and fast detection and location of faults are essential for increasing reliability and customer satisfaction at distribution levels). This has motivated the development of new and more efficient methods. Methods developed to detect and locate faults in power systems can be divided into two categories, conventional and artificial intelligence-based techniques. Although the utilization of artificial intelligence (AI) techniques offer tremendous potential, they are challenging and time consuming (i.e. many AI techniques require training data for processing). This paper presents a survey of the application of AI techniques to fault diagnosis (detection, classification and location of faults) of lines and cables of power systems at both transmission and distribution levels. The paper provides a short introduction to AI concepts, a brief summary of the application of AI techniques to power system analysis and design, and a discussion on AI-based fault diagnosis methods.

Figures

Figures reproduced from arXiv: 2507.10011 by the authors.

**Figure 7.** The transition from the traditional power grid to the smart grid. Technologies used to monitor the delivered energy allows for two-way communication between utilities and consumers, providing them with insight into their energy use. The integration of renewable power generation at a local level improves environmental performance and allows for two-way flow of electricity by enabling electricity to be transmitted fro… view at source ↗

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