Title :
Transmission line fault classification based on wavelet singular entropy and artificial immune recognition system algorithm
Author :
Zhu, Zhihui ; Sun, Yunlian
Author_Institution :
Sch. of Eng. & Technol., Huazhong Agric. Univ., Wuhan, China
Abstract :
The method based on wavelet singular entropy(WSE) and artificial immune recognition system (AIRS) for transmission line fault classification is presented in this paper. Wavelet singular entropy is used to quantify uncertainty of fault high frequency transient voltages so as to reflect and identify various failure states of power system. On this basis, AIRS for fault classification is presented to overcome the shortcomings of artificial neural network (ANN) and support vector machines (SVMs). The classifier can also decrease number of input parameters, relieve the dependence on prior knowledge of decision maker and improve generalization ability. The simulation results show the method is effective and correct.
Keywords :
neural nets; power engineering computing; power system faults; power transmission lines; support vector machines; wavelet transforms; ANN; SVM; artificial immune recognition system algorithm; artificial neural network; fault high frequency transient voltages; power system; support vector machines; transmission line fault classification; wavelet singular entropy; Artificial neural networks; Entropy; Fault diagnosis; Frequency; Power system simulation; Power system transients; Power transmission lines; Transmission lines; Uncertainty; Voltage; artificial immune recognition system; fault classification; singular entropy; wavelet packet;
Conference_Titel :
Power Electronics and Intelligent Transportation System (PEITS), 2009 2nd International Conference on
Conference_Location :
Shenzhen
Print_ISBN :
978-1-4244-4544-8
DOI :
10.1109/PEITS.2009.5407046