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A Novel Deep Convolution Neural Network and Spectrogram Based Microgrid Power Quality Disturbances Classification Method

With the integration of distributed generation systems, power electronic equipments, and non-linear loads, there are more risks of power quality disturbances (PQDs) in the microgrid. In order to ensure reliable and quality supply of power, the automatic classification of power quality is very important. In this paper, a novel deep learning method based on deep convolution neural network (CNN) and spectrogram for PQDs classification is proposed. Considering the characteristics of PQDs problems, the spectrogram is used to restructure PQDs waveforms, and the convolutional base, which consists of 2-D convolution, batch-normalization, dropout and max pooling layers, is designed to capture features, speed up training, and reduce overfitting. Through the convolutional base, the network can automatically extract features from waveforms of different time length. Moreover, a novel tagging method for PQDs is proposed and the small sample training is realized. The performance of the proposed method is evaluated on a set of synthetic single and combined PQDs waveforms.