A Temporal Ensembling Based Semi-Supervised Graph Convolutional Network for Power Quality Disturbances Classification

With the integration of multiple energy sources into the power grid makes power quality disturbances (PQDs) more complex. Dealing with power quality problems requires automatic classification of PQDs. This paper proposes a novel semi-supervised Graph Convolutional Network (GCN) framework based on Temporal Ensembling for PQDs classification. Considering both short-term and long-term features of PQDs, a Visibility Graph (VG) based graph theory model was adopted to process PQDs to highlight features. In the proposed semi-supervised framework, Graph Convolutional Network was designed to extract features from massive PQDs and classify PQDs automatically. Due to the fact that GCN belongs to supervised learning, it is necessary to label the data in advance. However, labeling is costly and easily lead to human mistake. Therefore, this article introduces the Temporal Ensembling algorithm which provides pseudo labels to reduce the amount of labeled data and has tolerance to incorrect labels. Simulation results prove that the proposed method is capable of noise resistance, tolerates incorrect labels, and has high classification performance in both single and composite PQDs.