Short-Time Power Quality Disturbances Identification Based on Improved Wavelet Energy Entropy and SVM
Li Gengyin, Wang Honglei, Zhou Ming
Key Laboratory of Power System Protection and Dynamic Security Monitoring and Control under Ministry of Education North China Electric Power University Beijing 102206 China
Abstract：This paper proposes an approach to identify short-time power quality disturbances based on improved wavelet energy entropy and support vector machine (SVM). Firstly, the sampled signals are processed by using the multi-scale wavelet resolution and reconstruction. Then, the sliding time window is introduced into algorithm, combined the time domain analysis with frequency domain analysis. The wavelet coefficients of the high frequency regions are selected for feature extraction. The values of the entropy are then calculated according to the improved wavelet energy entropy proposed in this paper as the disturbances features. Furthermore, these features are used as the input vectors of SVM to classify the short-time power quality disturbances. The simulation results show that the proposed method has merits over the conventional wavelet energy entropy approach.
李庚银, 王洪磊, 周明. 基于改进小波能熵和支持向量机的短时电能质量扰动识别[J]. 电工技术学报, 2009, 24(4): 161-167.
Li Gengyin, Wang Honglei, Zhou Ming. Short-Time Power Quality Disturbances Identification Based on Improved Wavelet Energy Entropy and SVM. Transactions of China Electrotechnical Society, 2009, 24(4): 161-167.
2009, Vol. 24 
