A Method to Identify PQD Based on SVM and Wavelet Energy Distribution

摘要：

Approached a method to identify power quality disturbance (PQD) type based on support vector machine(SVM) and improved wavelet energy distribution. Firstly, using wavelet transform to analyse PQD signals, extracting disturbance lasting time and energy differences of each level between PQD signal and standard signal as feature vectors, forming die training samples and testing samples. Secondly, pre-process the training set by using neighbourhood rough set model to delete those abnormal samples and disturbances. Lastly, train die PQD samples by using binary tree SVM (BT-SVM) to identify PQD signals. Simulation results indicate that the proposed method can identify seven PQD signals and sinusoidal signal, having an excellent performance on correct ratio(the average ratio can reach 92.03 percent), having high identify speed and strong resistance to noise, and is very suitable for PQD identification system.

关键词： energy smpport vector machine disturbance identification wmvelet energy distribution neighborhood rough set

作者: CHEN Zhen-ping LIU Huai-xia

作者单位: Anhui University of Science and Technology,Anhui, Huainan,232001, China

会议类型: 国际会议

会议名称: 2011 Fourth International Conference on Intelligent Computation Technology and Automation(2011年第四届智能计算技术与自动化国际会议 ICICTA 2011)

会议地点: 深圳

会议语种:英文

页码: 23-26

在线出版日期: 2011-03-28（万方平台首次上网日期，不代表论文的发表时间）