Multi-Label Classification for Power Quality Disturbances by Integrated Deep Learning

Abstract

The traditional power quality disturbances classification methods include three stages, i.e., feature extraction, feature selection, classifier training. These methods suffer from low accuracy and a limited improvement margin. Since deep learning can greatly improve the accuracy of classification, a new classification method was designed in this paper by combining three types of deep learning frameworks, including CNN-GRU, ResNet-GRU, and Inception-GRU. The proposed method omits the two steps of feature extraction and feature selection, achieving “end-to-end” PQDs identification. To improve the performance on real signals, “pre-training and re-training” is applied. Then, a voting method was employed to vote the prediction labels by different algorithms, which further improves the accuracy of classification. Simulation experiments show that for the classification of compound PQDs, the proposed method performs better than the triple-stage methods and single deep learning classification method. Finally, real signals from Power source are test by the twice-trained model, and the five metrics are better than the old methods.

Keywords

• Power quality disturbances
• deep learning
• CNN
• ResNet
• multi-label learning