Scholarlink

<p>Fault localization in power systems is a critical challenge, particularly with the increasing complexity introduced by distributed energy resources and smart grid technologies. This paper presents an in-depth review and comparative analysis of prominent fault localization methodologies, addressing the current gap in comprehensive studies that assess both performance and implementation complexity. Focusing on the period from 2020 to 2024, our review synthesizes findings on six key methods: impedance-based, traveling wave-based, machine learning (ML)-based, voltage sag-based, PMU-based techniques and power quality-based methods. Comparative evaluation indicates that impedance-based techniques maintain localization accuracy of 1–5%, while hybrid ML–signal models achieve sub-1% average error with 30–40% reduction in computational time in benchmark scenarios. Our analysis reveals that while advanced ML and traveling wave methods offer superior accuracy and real-time capability, they often come with significant implementation challenges. Conversely, traditional impedance-based methods, while simpler, are limited by high-resistance fault scenarios. This review provides valuable insights for researchers and engineers, identifying key trade-offs and highlighting the trajectory of future research towards the development of adaptive, data-driven hybrid systems that leverage the strengths of multiple approaches.</p>

错误：搜索内容不能为空，请输入英文关键词
错误：关键词超出字数限制，请精简
高级检索

Critical review of fault localization techniques in the age of smart grids: performance, complexity, and the path to hybrid solutions

  • 期刊论文

  • 发表日期：

    2025年11月27日

查看全文
  • Lingyao Xu,
  • Mohd Syukri Ali,
  • Tan Chia Kwang,
  • Ab Halim Abu Bakar,
  • Issam A. Smadi,
  • Saher Albatran

摘要

Fault localization in power systems is a critical challenge, particularly with the increasing complexity introduced by distributed energy resources and smart grid technologies. This paper presents an in-depth review and comparative analysis of prominent fault localization methodologies, addressing the current gap in comprehensive studies that assess both performance and implementation complexity. Focusing on the period from 2020 to 2024, our review synthesizes findings on six key methods: impedance-based, traveling wave-based, machine learning (ML)-based, voltage sag-based, PMU-based techniques and power quality-based methods. Comparative evaluation indicates that impedance-based techniques maintain localization accuracy of 1–5%, while hybrid ML–signal models achieve sub-1% average error with 30–40% reduction in computational time in benchmark scenarios. Our analysis reveals that while advanced ML and traveling wave methods offer superior accuracy and real-time capability, they often come with significant implementation challenges. Conversely, traditional impedance-based methods, while simpler, are limited by high-resistance fault scenarios. This review provides valuable insights for researchers and engineers, identifying key trade-offs and highlighting the trajectory of future research towards the development of adaptive, data-driven hybrid systems that leverage the strengths of multiple approaches.