Power Quality Disturbance Classification Based on Growing and Pruning Optimal RBF Neural Network
WANG Huihui1, 2, WANG Ping1, LIU Tao3, ZHANG Bowen1
1. School of Electrical and Information Engineering, Tianjin University, Nankai District, Tianjin 300072, China; 2. School of Control and Mechanical Engineering, Tianjin Chengjian University, Xiqing District, Tianjin 300384, China; 3. School of Electrical Engineering and Automatic, Tianjin Polytechnic University, Xiqing District, Tianjin 300387, China
Abstract: To set the centers, widths and output weights of neurons in hidden layer of radial basis function (RBF) neural network, a new algorithm for power quality disturbance classification using growing and pruning (GAP) RBF neural network is proposed. Firstly, the paper establishes a mathematical model of power quality disturbances, adopting GAP algorithm to optimize the parameters of RBF neural network and design flow chart of power quality disturbances classification algorithm. Secondly, generalized S-transform, feature extraction and GAP-RBF neural network are used to process eight types of power quality disturbances. With help of simulation analysis, ability of GAP-RBF neural network to optimize the hidden layer of neuron is verified, and the range of parameters for optimal algorithm are provided. Simulation and experiment results show that, compared with other similar algorithms, this algorithm can reduce the number of hidden neurons while ensuring accuracy of disturbance classification, and realize self-optimization of parameters and inherited learning of RBF neural network.
王慧慧, 王萍, 刘涛, 张博文. 基于生长-修剪优化RBF神经网络的电能质量扰动分类[J]. 电网技术, 2018, 42(8): 2408-2415.
WANG Huihui, WANG Ping, LIU Tao, ZHANG Bowen. Power Quality Disturbance Classification Based on Growing and Pruning Optimal RBF Neural Network. POWER SYSTEM TECHNOLOGY, 2018, 42(8): 2408-2415.
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