Title :
Power System Fault Identification Method Based on Multi-wavelet Packet and Artificial Neural Network
Author :
Wang Ke ; Chen Weirong ; Li Qi
Author_Institution :
South west Jiao Tong Univ., Chengdu, China
Abstract :
The fault identification of power system is of great significance in the event of failure This paper introduce a fault identification method based on multi-wavelet packet and artificial neural network. Firstly, through the simulation of a two-500Kv power source transmission line on PSCAD/EMTDC, the variety of fault signals is generated in different conditions. Then, these fault signals are decomposed appropriately by multi-wavelet packets. Therefore, the energy features of the fault signals in each frequency band are obtained. BP neural network is trained by suitable training sample. Finally, each fault type can be automatically identified through combining multi-wavelet packet and BP neural network. From the results, the method is effective to identify the fault of high-voltage AC transmission line in power systems.
Keywords :
backpropagation; fault diagnosis; neural nets; power engineering computing; power transmission faults; power transmission lines; wavelet transforms; BP neural network; PSCAD-EMTDC; artificial neural network; fault signals; high-voltage AC transmission line; multiwavelet packet; power source transmission line; power system fault identification method; voltage 500 kV; Biological neural networks; Circuit faults; Fault diagnosis; Grounding; Power transmission lines; Resistance; Training; BP neural network; Fault classification; Multi-wavelet;
Conference_Titel :
Intelligent System Design and Engineering Application (ISDEA), 2012 Second International Conference on
Conference_Location :
Sanya, Hainan
Print_ISBN :
978-1-4577-2120-5
DOI :
10.1109/ISdea.2012.421
