Power Quality Disturbance Recognition Based on Wavelet Transform and Convolutional Neural Network

Power quality (PQ) interference has caused many adverse effects on industry and life. In order to improve the accuracy of power quality disturbance identification, a hybrid detection method based on wavelet transform and convolutional neural network is proposed in this paper, which is for the recognition of power quality disturbance. Wavelet transform can extract the time-frequency domain features of perturbation signals, and convolutional neural network can recognize and classify these features. In order to test the performance of the proposed method, several experiments have been conducted. Firstly, mathematical modelling for seven kinds of power quality disturbances is carried out by this paper. Secondly, identification experiments is processed. Finally, some common methods are used as comparison to experiments. The obtained experimental results reveal that the proposed method has high accuracy and stable performance.