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

Yuanyu Ye,Yan Li,Aichao Yang,Yu Wu,Chen Hu,Min Li,Xiaosong Deng

doi:10.1155/2020/9867985

Abstract

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.

Highlights

As a key equipment for signal acquisition in the power system, the transformer provides a reliable basis for electric energy measurement, condition monitoring, and relay protection. e electronic transformer (ET), as the important equipment for digitizing electric parameter information in the smart grid, has been highly valued
During the actual operation process of ET, it is necessary to accurately and quickly diagnose the abnormality of the measurement error, and to make timely predictions on the deterioration trend of measurement error, so that the related personnel can carry out the inspection and maintenance and reduces the loss of electric energy measurement and ensures the normal operation of the monitoring protection device, which is of great significance for ensuring the safe, stable, and economical operation of the power system
In terms of prediction results, the backpropagation neural network (BPNN) only predicts the general trend of the ET phase error in the 8 days

Summary

Introduction

As a key equipment for signal acquisition in the power system, the transformer provides a reliable basis for electric energy measurement, condition monitoring, and relay protection. e electronic transformer (ET), as the important equipment for digitizing electric parameter information in the smart grid, has been highly valued. Erefore, under the situation of no standard transformer, making full use of the measurement data of ET in the smart grid, this paper analyses and simulates the development trend of ET error through the backpropagation neural network (BPNN) and the Prophet model, respectively, so as to ensure the safe and stable operation of ET. E data processing unit, which meets the accuracy requirement of 0.05 level, receives the output signal and sampled value message data of the signal acquisition unit and obtains the error comparison result by taking the output of the electromagnetic current transformer as the standard. On 13th to 21st, the median line is in the middle, and the phase error presents normal distribution; at the beginning and the end of the month, the median line is shifted upwards, and the phase error presents skew

Data points

Output layer

Setting value

Findings

Day of week