Online Evaluation Method of CVT Internal Insulation Abnormality Based on Self-Supervised Learning

Abstract

A capacitive voltage transformer (CVT) is one of the electrical quantities measurement devices, and the state of its internal insulation is the key factor for ensuring the accuracy of its measurement of electrical energy. In view of the fact that the traditional real-time evaluation method of a CVT internal insulation anomaly mainly relies on empirical rules and prior knowledge and lacks the ability to independently mine effective features, an online evaluation method of a CVT internal insulation anomaly based on self-supervised learning is proposed. Firstly, an autoencoder is constructed to extract the residual sequence of the CVT secondary voltage and eliminate the influence of primary voltage fluctuation and power system voltage regulation. Without any prior knowledge, the complex dependence of the residual sequences in time and feature dimensions is learned by using a parallel graph attention layer (GATv2). Finally, a joint optimization based on the prediction and reconstruction model is introduced to obtain the abnormal inference score at each timestamp and realize the evaluation of the CVT internal insulation status. Experimental analysis shows that this method can effectively eliminate the influence of primary voltage fluctuation and power system voltage regulation on the online evaluation of the CVT internal insulation status and independently excavate the abnormal characteristics of the CVT secondary voltage to realize real-time monitoring and early warning of the CVT internal insulation status.