Classification of Power Quality Disturbances Using S-Transform Based Artificial Neural Networks

摘要：

This paper presents a method based on S-transform and artificial neural network for detection and classification of power quality disturbances. The input features of the neural network are extracted using S-transform. The features obtained from the Stransform are distinct, understandable and immune to noise. These features after normalization are given to a feed forward neural network trained by the back propagation algorithm. The data required to develop the network are generated by simulating various faults in a test system. The proposed method requires less number of features and less memory space without losing its original property. The simulation results show that the proposed method is effective and can classify the power quality signals even under noisy environment.

关键词： power quality disturbance S-transform artificial neural network wavelet transform

作者: Suriya Kaewarsa

作者单位: Department of Electrical Engineering,Faculty of Industry and Technology Rajamangala University of Technology Isan,Sakon Nakhon Campus Sakon Nakhon,Thailand

会议类型: 国际会议

会议名称: 2009 IEEE International Conference on Intelligent Computing and Intelligent Systems(2009 IEEE 智能计算与智能系统国际会议)

会议地点: 上海

会议语种:英文

页码: 566-570

在线出版日期: 2009-11-20（万方平台首次上网日期，不代表论文的发表时间）