High-impedance fault detection method based on sparse data divergence discrimination in distribution networks

Cui, Laixi; Yang, Lijun; Wang, Lei; Chen, Jian; Xue, Zhang

doi:10.1016/j.epsr.2023.109514

Cited by 8 publications

(1 citation statement)

References 35 publications

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“…Moreover, the reviews do not provide in-depth analysis. The work should be extended to include further papers and the broad domain should be further classified [44][45][46][47][48]. The classification may be done based on the type of fault being detected, the power equipment of the network for which the fault is being detected, the AI method used, etc.…”

Section: Limitation and Future Workmentioning

confidence: 99%

Fault Detection and Fault Diagnosis in Power System Using AI: A Review

Nasim,

Aziz,

Qaiser

et al. 2024

SSURJET

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Electricity, which is essential to modern society, necessitates a consistent and uninterrupted supply. Faults in power systems pose difficulties, highlighting the vital importance of fault identification and diagnosis. This review paper provides a concise overview of artificial intelligence-based fault detection and diagnosis in power systems. The primary focus is on deep learning; on the one hand, it compares various works and acts as a primer for those who are unfamiliar with them. On the other hand, it delves into the application of UV-visible video processing to detect incipient faults by analyzing corona discharge and air ionization. Moreover, this state-of-the-art work highlights deep learning applications, particularly in UV-visible video processing, with the goal of detecting incipient faults through corona discharge and air ionization analysis. Despite ongoing research, the field lacks a clear path and structure, emphasizing the need for continued advancement in utilizing AI for effective fault detection and diagnosis in power systems.

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Section: Limitation and Future Workmentioning

confidence: 99%