Application of Neural Network Combined with Improved Algorithm in Distorted Waveform Analysis
The issue of power quality has attracted considerable attention from utilities and customers due to fast development of power system industry. It is vital to ensure power quality by means of advanced technology which can monitor, locate and classify disturbances with measurement approaches and instruments. A novel approach for the power quality disturbances detection and investigation using wavelet transform and neural network is proposed. The wavelet transform technology provides an effective means for analysis of non-stationarity and transient signal in terms of shifted and scaled versions. The detection and localization processes are a series of convolution and decimation processes at each corresponding scale, which provide feature vectors as input variables for neural network designed for disturbance pattern recognition. The wavelet network combines advantages of wavelet transformation for purposes of feature extraction and with the characteristic decision capabilities of neural network. In process of training phase, the evolutionary algorithm is used to complete the network parameters adjustment. The processing phase performs waveform recognition and the output of the processing phase is the type of the disturbances. The simulation results show that the proposed method has the ability of recognizing and classifying different power disturbance types efficiently.
Power quality disturbance wavelet transform neural network pattern recognition feature extraction evolutionary algorithm
Wei Liao Hua Wang Pu Han
Hebei University of Engineering, Handan 056038, China North China Electric Power University, Baoding 071003, China
国际会议
The 22nd China Control and Decision Conference(2010年中国控制与决策会议)
徐州
英文
2525-2528
2010-05-26（万方平台首次上网日期，不代表论文的发表时间）