Error prediction of a capacitor voltage transformer using dilated causal convolution and LSTM

Abstract

To timely and accurately complete the error prediction of capacitor voltage transformers, this paper proposes an error prediction algorithm for capacitor voltage transformers that combines dilated causal convolution and LSTM. The algorithm is divided into two parts: one part updates the state of the input sequence using LSTM, while the other part extracts features through dilated causal convolution, maintaining data causality about the original sequence. Finally, the outputs of these two parts are combined to make the obtained sequence feature information more accurate and enriched. Experiments show that the MSE between the error prediction results obtained by this method and the actual error samples of capacitor voltage transformers is only 0.0003, which can accurately and efficiently complete the error prediction of capacitor voltage transformers.