Ensemble deep learning for automated classification of power quality disturbances signals

Abstract

• A deep learning framework for intelligent classification of PQD signals is proposed. • The improved Bagging algorithm has better accuracy and generalization performance. • The active learning strategy selects the most representative samples from a dataset. • The active learning strategy uses less labeled data thus saving manual labeling cost. The automatic classification of power quality disturbances (PQD) is of great significance for solving power quality problems. In this study, we propose an ensemble deep learning framework to realize intelligent classification of PQ disturbances. Specifically, based on the characteristics of the sequence of disturbance signals, the Long Short Term Memory (LSTM) network is used to classify the signals. In addition, the Bagging theory is used to integrate the training results of multiple LSTM networks to improve the generalization of the network. Our contribution lies in the combination of deep learning and ensemble learning to extract the classification representation of PQD signals. In view of the large number of unlabeled power quality disturbance samples in the power grid, the active learning strategy is adopted to select the most representative samples from the data set, which can enhance the model performance with less labeled data. Finally, experiments were conducted in different noise environments. Compared with the existing multi-label learning models, this method achieves better classification performance with good calculation speed. Furthermore, the proposed active learning strategy is able to train the classification model with fewer labeled samples, reducing the manual labeling costs.