TY - JOUR
T1 - A hybrid framework for fault detection, classification, and location-Part I
T2 - Concept, structure, and methodology
AU - Jiang, Joe Air
AU - Chuang, Cheng Long
AU - Wang, Yung Chung
AU - Hung, Chih Hung
AU - Wang, Jiing Yi
AU - Lee, Chien Hsing
AU - Hsiao, Ying Tung
PY - 2011/7/1
Y1 - 2011/7/1
N2 - 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.
AB - 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.
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U2 - 10.1109/TPWRD.2011.2141157
DO - 10.1109/TPWRD.2011.2141157
M3 - Article
AN - SCOPUS:79959766001
VL - 26
SP - 1988
EP - 1998
JO - IEEE Transactions on Power Delivery
JF - IEEE Transactions on Power Delivery
SN - 0885-8977
IS - 3
M1 - 5771142
ER -