Machine learning for fault diagnosis in electrical systems: systematic review of applications and trends
Keywords:
machine learning, deep learning, fault diagnosis, electrical power systems, systematic reviewAbstract
Introduction: Fault diagnosis is essential for the safe and reliable operation of electrical power systems, while machine learning has become a central approach for automating detection, classification and localization tasks.
Objective: This review identifies and synthesizes applications and methodological trends reported between 2019 and 2022.
Method: A structured literature review was conducted in Scopus, Web of Science, PubMed and SciELO, using explicit eligibility criteria and a narrative thematic synthesis. Thirty-five publications formed the final corpus. The review was guided by PRISMA 2020 reporting principles; however, intermediate screening counts were not retrospectively reconstructed because they had not been recorded during the original search.
Results: The evidence shows a transition from support vector machines, artificial neural networks and other shallow-learning methods toward convolutional and recurrent deep-learning architectures. Power transformers, transmission lines and distribution networks were the most developed application domains. Transfer learning, hybrid models, multisensor diagnosis, interpretability and cybersecurity emerged as relevant research directions. Reported accuracies were high in several selected studies, but comparisons are limited by heterogeneous datasets, signals, validation protocols and metrics, and much of the evidence derives from laboratory, simulation or benchmark settings.
Conclusions: Machine learning has substantial diagnostic potential, but industrial adoption requires external validation, standardized evaluation, transparent reporting, robust data governance and stronger evidence under real, variable and operationally demanding conditions across different electrical assets.
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