A Novel Deep Learning Power Quality Disturbance Classification Method using Autoencoders

Automatic identification and classification of power quality disturbances (PQDs) is crucial for maintaining efficiency and safety of electrical systems and equipment condition. In recent years emerging deep learning techniques have shown potential in performing classification of PQDs. This paper proposes two novel deep learning models, called CNN(AE)-LSTM and CNN-LSTM(AE) that automatically distinguish between normal power system behaviour and three types of PQDs: voltage sags, voltage swells and interruptions. The CNN-LSTM(AE) model achieved the highest average classification accuracy with a 65:35 train-test split. The Adam optimiser and a learning rate of 0.001 were used for ten epochs with a batch size of 64. Both models are trained using real world data and outperform models found in literature. This work demonstrates the potential of deep learning in classifying PQDs and hence paves the way to effective implementation of AI-based automated quality monitoring to identify disturbances and reduce failures in real world power systems.