A Method of Real-Time Power Quality Disturbance Classification
Chen Xiaojing1,2, Li Kaicheng1, 3, Xiao Jian4, Meng Qingxu1, Cai Delong1
1.State Key Laboratory of Advanced Electromagnetic Engineering and Technology Huazhong University of Science and Technology Wuhan 430074 China 2.College of Electronic and Information Yangtze University Jingzhou 434023 China 3.Hubei Collaborative Innovation Center for High-efficient Utilization of Solar Energy Hubei University of Technology Wuhan 430064 China 4.State Grid Hunan Electric Power Company Electric Power Research Institute Changsha 410007 China
Abstract：In order to meet the requirements of classifying power-quality disturbances in real time, this paper proposes a new method based on the strong tracking filter (STF) and the extreme learning machine (ELM). STF is the modified version of the extended Kalman filter (EKF) by introducing the fading factor matrix to solve the problem of divergence. STF can not only detect the amplitude of the fundamental but also provide the fading factor as a feature identifying transient disturbances and harmonics. The proposed feature vector sets were composed of four features including the maximum and the minimum of the fundamental amplitude, the number of fluctuations, and the mean value of the fading factor frequentness. They were input into the ELM as the training examples to obtain a classifier for identifying disturbances. In addition, some rules were used to correct the error classification in a few boundary samples for attaining the higher accuracy. The simulation results show that the proposed method can identify 10 types of power quality disturbances including two complex disturbances, and have good noise immunity. And the higher accuracy can be achieved with less training and testing time compared with the stochastic gradient descent back-propagation (SGBP), least square support vector machine (LSSVM) and online sequential extreme learning machine method (OSELM). The proposed method is suitable for the online application.
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2017, Vol. 32 
