A Hybrid Framework for Fault Detection, Classification, and Location-Part I: Concept, Structure, and Methodology

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 tr...

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Veröffentlicht in:IEEE transactions on power delivery 2011-07, Vol.26 (3), p.1988-1998
Hauptverfasser: JIANG, Joe-Air, CHUANG, Cheng-Long, WANG, Yung-Chung, HUNG, Chih-Hung, WANG, Jiing-Yi, LEE, Chien-Hsing, HSIAO, Ying-Tung
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container_end_page 1998
container_issue 3
container_start_page 1988
container_title IEEE transactions on power delivery
container_volume 26
creator JIANG, Joe-Air
CHUANG, Cheng-Long
WANG, Yung-Chung
HUNG, Chih-Hung
WANG, Jiing-Yi
LEE, Chien-Hsing
HSIAO, Ying-Tung
description 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 methods-including multilevel wavelet transform, principal component analysis, support vector machines, and adaptive structure neural networks-are 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.
doi_str_mv 10.1109/TPWRD.2011.2141157
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identifier ISSN: 0885-8977
ispartof IEEE transactions on power delivery, 2011-07, Vol.26 (3), p.1988-1998
issn 0885-8977
1937-4208
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source IEEE Electronic Library (IEL)
subjects Applied sciences
Artificial neural networks (ANNs)
Classification
Electric potential
Electrical engineering. Electrical power engineering
Electrical power engineering
Exact sciences and technology
Fault detection
fault location
Faults
Joints
Mathematical models
Methods
Miscellaneous
Multiresolution analysis
Networks
Neural networks
Position (location)
Power networks and lines
Power transmission lines
principal component analysis (PCA)
Studies
support vector machine (SVM)
Testing. Reliability. Quality control
Transmission line measurements
Voltage
Wavelet transforms
title A Hybrid Framework for Fault Detection, Classification, and Location-Part I: Concept, Structure, and Methodology
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