Automatic classification of single and hybrid power quality disturbances using Wavelet Transform and Modular Probabilistic Neural Network

Power Quality (PQ) monitoring in a systematic and automated way is the important issue to prevent detrimental effects on power system. The development of new methods for the automatic classification of PQ disturbances is at present a major concern. This paper presents a novel approach of automatic d...

全面介绍

Saved in:
书目详细资料
Main Authors: Khokhar, S., Mohd. Zin, A. A., Mokhtar, A. S., Bhayo, M. A., Naderipour, A.
格式: Conference or Workshop Item
出版: Institute of Electrical and Electronics Engineers Inc. 2016
主题:
在线阅读:http://eprints.utm.my/id/eprint/73415/
https://www.scopus.com/inward/record.uri?eid=2-s2.0-84964345645&doi=10.1109%2fCENCON.2015.7409588&partnerID=40&md5=425fedc47256eac67d499a68e860dfd9
标签: 添加标签
没有标签, 成为第一个标记此记录!
实物特征
总结:Power Quality (PQ) monitoring in a systematic and automated way is the important issue to prevent detrimental effects on power system. The development of new methods for the automatic classification of PQ disturbances is at present a major concern. This paper presents a novel approach of automatic detection and classification of single and hybrid PQ disturbances using Discrete Wavelet Transform (DWT) and Modular Probabilistic Neural Network (MPNN). The automatic classification of the PQ disturbances consists of three stages i) data generation, ii) feature extraction and iii) disturbance classification. The data is generated by synthetic models of single and hybrid PQ disturbance signals based on IEEE 1159 standard. DWT with multiresolution analysis was applied for feature extraction from the PQ waveforms. The entropy and energy features extracted from the detail and approximation coefficients were applied as the training and testing data to MPNN in order to accomplish the automatic classification process. The effectiveness of the proposed algorithm has been validated by using a typical real-time underground distribution network in Malaysia which was simulated in PSCAD/EMTDC power system software to generate PQ disturbances. The simulation results show that the classifier has an excellent performance in terms of accuracy and reliability even in the case of PQ signals under noisy condition.