The measurement error prediction method of voltage transformer based on TCN-RBF

Abstract

Abstract Accurate prediction of voltage transformer error is an important guarantee for the stable and economic operation of the power grid. This paper presents a measurement error prediction method for voltage transformers based on time convolution and radial basis neural networks (TCN-RBF). Firstly, the ensemble empirical mode decomposition is used to decompose the original error sequence into multiple components. Secondly, a TCN prediction model was established based on each component and historical error sequence to achieve error prediction. To further improve the prediction progress, the RBF model was used to further fit the predicted results with the environmental parameter data and confirm the final predicted value. The operation error of the transformer in a substation is tested. The test results show that compared with LSTM, SVM, BP, and other prediction models, this method has better performance in error prediction.