Short-Term Prediction of Electronic Transformer Error Based on Intelligent Algorithms

As the key metering equipment in the smart grid, the measurement accuracy and stability of electronic transformer are important for the normal operation of power system. In order to solve the problem that there is no effective way to predict the error developing trend of electronic transformer, this paper proposed two kinds of short-term prediction methods for electronic transformer error based on the backpropagation neural network and the Prophet model, respectively. First, preprocessing and visualization operation are performed on the original error data. Then, the data fitting and short-term prediction of electronic transformer error are made on the basis of the backpropagation neural network and the Prophet model, and the fitting and prediction results of the two methods are compared and analysed in combination with four evaluation indexes. Finally, the Prophet model is adopted to simulate the development trend and periodic fluctuation of error, and the reason for fluctuation is analysed. The simulation results show that the Prophet model is more suitable for the prediction of electronic transformer measurement error than the backpropagation neural network.