1. College of Electrical Engineering and Automation, Fuzhou University, Fuzhou 350108; 2. Department of Electrical Engineering, Yuan Ze University, Chung Li, Taiwan 32003; 3. State Grid Fujian Jinjiang County Electric Power Supply Co., Ltd, Quanzhou, Fujian 362200;
Abstract：Faulty feeder detection timely and accurately in resonant grounding distribution systems is still a focus of research. The conventional methods commonly use single faulty feeder detection methods, such as wavelet transform method, transient energy method, and the fifth harmonic current method, ect. However, their reliability is not satisfied due to the partial fault information is considered. A novel approach to identify the faulty feeder based on discrete wavelet packet transform (DWPT) and machine learning is proposed in this paper. The time-frequency matrices are acquired by utilizing the DWPT to the collected transient zero-sequence current signals of the faulty feeder and sound feeders. The feature vectors will be extracted manually by calculating time-frequency matrices with statistical quantities. The Adaboost classifier is trained by a large number of feature vectors under various kinds of fault conditions and factors. The faulty feeder detection can be achieved by the trained two classifiers. A PSCAD/EMTDC simulator is established to simulate a practical 10kV resonant grounding distribution system. Verification results of the testing cases reveal that the proposed approach of fault protection is able to achieve good identification accuracy.
2018, Vol. 19 
