A novel fault classification technique for double-circuit lines based on a combined unsupervised/supervised neural network

被引:61
作者
Aggarwal, RK
Xuan, QY
Dunn, RW
Johns, AT
Bennett, A
机构
[1] Univ Bath, Dept Elect & Elect Engn, Bath BA2, Avon, England
[2] Reyrolle Protect, Hebburn NE31 1TZ, Tyne & Wear, England
关键词
fault classification; double circuit transmission lines; combined unsupervised/supervised learning; self-organization mapping; neural networks;
D O I
10.1109/61.796214
中图分类号
TM [电工技术]; TN [电子技术、通信技术];
学科分类号
0808 ; 0809 ;
摘要
The work described in this paper addresses the problems encountered by conventional techniques in fault type classification in double-circuit transmission lines; these arise principally due to the mutual coupling between the two circuits under fault conditions, and this mutual coupling is highly variable in nature. It is shown that a neural network based on combined unsupervised/supervised training methodology provides the ability to accurately classify the fault type by identifying different patterns of the associated voltages and currents. The technique is compared with that based solely on a supervised training algorithm (ie back-propagation network classifier). It is then tested under different fault types, location, resistance and inception angle; different source capacities and load angles are also considered. All the test results show that the proposed fault classifier is very well suited for classifying fault types in double-circuit lines.
引用
收藏
页码:1250 / 1256
页数:7
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