A hybrid framework for fault detection, classification, and location-Part I: Concept, structure, and methodology

Joe Air Jiang, Cheng Long Chuang, Yung Chung Wang, Chih Hung Hung, Jiing Yi Wang, Chien Hsing Lee, Ying Tung Hsiao

研究成果: Article同行評審

79 引文 斯高帕斯(Scopus)

摘要

Bridging the gap between the theoretical modeling and the practical implementation is always essential for fault detection, classification, and location methods in a power transmission-line network. In this paper, a novel hybrid framework that is able to rapidly detect and locate a fault on power transmission lines is presented. The proposed algorithm presents a fault discrimination method based on the three-phase current and voltage waveforms measured when fault events occur in the power transmission-line network. Negative-sequence components of the three-phase current and voltage quantities are applied to achieve fast online fault detection. Subsequently, the fault detection method triggers the fault classification and fault-location methods to become active. A variety of methodsincluding multilevel wavelet transform, principal component analysis, support vector machines, and adaptive structure neural networksare incorporated into the framework to identify fault type and location at the same time. This paper lays out the fundamental concept of the proposed framework and introduces the methodology of the analytical techniques, a pattern-recognition approach via neural networks and a joint decision-making mechanism. Using a well-trained framework, the tasks of fault detection, classification, and location are accomplished in 1.28 cycles, significantly shorter than the critical fault clearing time.

原文English
文章編號5771142
頁(從 - 到)1988-1998
頁數11
期刊IEEE Transactions on Power Delivery
26
發行號3
DOIs
出版狀態Published - 2011 七月 1

All Science Journal Classification (ASJC) codes

  • Energy Engineering and Power Technology
  • Electrical and Electronic Engineering

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