Wavelet Change and Convolutional Neural Network Based Power Quality Online State Estimation Method

The large-scale integration of new energy power generation and its supporting facilities in power system makes power quality online monitoring system facing new challenges like processing massive monitoring data which makes power quality data feature extraction and state estimation more difficult. In order to meet the data-driven based real-time power quality management requirements, this paper proposed an online feature extraction and state estimation method for power quality data processing. First, characteristic values of power quality monitoring data are calculated using wavelet change based feature extracted method. Second, extracted feature model is established according to multiple eigenvalues of power quality disturbance training examples. Finally, dynamic classification and power quality online state estimation method is proposed using convolution neural network. The simulation results verifies the feasibility and efficiency of the proposed method.