Feature Selection and Classification of Power Quality Problem

• Main Authors: Yi-Xing Shen, 沈玴興
• Other Authors: Chun-Yao Lee
• Format: Others
• Language: zh-TW
• Published: 2010
• Online Access: http://ndltd.ncl.edu.tw/handle/35663727469881078024

碩士 === 中原大學 === 電機工程研究所 === 98 === With the rapid development of technology in the current society, precise electronic control equipment has been an indispensable role in our daily life. However, these control components are considerably sensitive in accordance with the quality requirement of power supply, that is, degraded power quality could cause malfunction and even lead to a permanent damage of equipment. Nowadyas, any component damage could cause expended damage easily due to the highly interated power network. Therefore, it is a critical issue that power transmission and distribution should be improved. First, this paper obtains time-frequency and time-time relationships of 11 types of power quality disturbance (PQD) by using S-transform (ST) and TT-transform (TT). By observing the ST and TT contours, 6 types of time characteristic curves and 5 types of frequency characteristic curves are depicted. According to the ST contour and the 11 types of characteristic curves, 62 candidate features are calculated for describing the PQD waveforms. Second, a probabilistic neural network (PNN) based feature selection scheme is constructed. Simultaneously, the fully informed particle swarm (FIPS) is applied to optimize smoothing parameter matrix and the leave-one-out cross validation is applied to estimate classification accuracy of PNN. The least influenced features are removed from the 62 candidate features in the condition of not degrading the cross-validation accuracy so as to reconstruct a desired feature vector. Finally, the PNN-based feature selection scheme is implemented to obtain new feature vector in the conditions of no noise, SNR=30dB, and SNR=20dB, respectively. The PNN, multi-layer perceptorn (MLP), and K-nearest neighbor (KNN) are tested. The results have shown that the PNN-based feature selection scheme can be applied to highten the classification accuracy of MLP and KNN in the conditions of no noise, SNR=30dB, and SNR=20dB, particulary in the condition of SNR=20dB. Moreover, the classification accuracy of MLP is the highest among the three.