Novel approach of fault type classification in transmission lines based on rough membership neural networks

作者：Lin Sheng^*; He Zhengyou; Zang Tianlei; Qian Qingquan

来源：Proceedings of the CSEE, 2010, 30(28): 72-79.

• Fault types
• Feature extracting
• Power systems
• Power transmission line
• Rough membership

A novel approach to fault classification of transmission lines based on rough membership neural networks (RMNN) is presented. Ten RMNNs are designed to classify ten kinds of fault type such as single phase to ground faults (Ag, Bg, Cg), phase to phase to ground faults (ABg, BCg, CAg), phase to phase faults (AB, BC, CA), and three phase fault (ABC). In order to reduce the training time and cases of artificial neural network, the input layer of rough membership neural networks consist of rough neurons while the hidden layer and output layer consist of fuzzy neurons. The distinctive time domain features and time-frequency domain features are extracted from only quarter period of post-fault current signals and fed into the rough membership neural networks for classifying faults. Extensive computer simulation has been conducted using PSCAD/EMTDC. Results show that the approach provides an accepted degree of accuracy in fault classification under various fault conditions. ? 2010 Chin. Soc. for Elec. Eng.

• 出版日期2010
• 单位西南交通大学