Abstract:

Aiming at the problems of difficult manual selection of features, cumbersome classification steps and low accuracy in traditional power quality disturbance classification methods, a disturbance classification method based on particle swarm optimization (PSO) and convolutional neural network (CNN) was proposed. Firstly, the one-dimensional time series of power quality disturbance signals were converted into two-dimensional matrices with equal rows and columns by using the reshaping function, and these two-dimensional matrices were properly divided into training data set and test data set. Secondly, the classification model of power quality disturbance was built based on CNN. Thirdly, the PSO algorithm was used to optimize the parameters of the classification model, and the trained data set was used to train the optimized power quality disturbance classification model. Finally, the trained power quality disturbance classification model was tested by using the test data set, and the class results of various power quality disturbances were obtained according to the output labels. Simulation results show that the classification model can extract the characteristics of power quality disturbance data by itself. Compared with other power quality disturbance classification models, this method has higher classification accuracy for power quality disturbance signals.

Key words: new energy, power quality, disturbance classification, feature extraction, particle swarm optimization (PSO), deep learning, convolution neural network (CNN)