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...

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Main Authors: Yuanyu Ye, Aichao Yang, Yu Wu, Chen Hu, Min Li, Yan Li, Xiaosong Deng
Format: Article
Language:English
Published: Hindawi Limited 2020-01-01
Series:Journal of Control Science and Engineering
Online Access:http://dx.doi.org/10.1155/2020/9867985
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spelling doaj-c43d988af2194a75a2586c4c8507fbdc2020-11-25T01:58:55ZengHindawi LimitedJournal of Control Science and Engineering1687-52491687-52572020-01-01202010.1155/2020/98679859867985Short-Term Prediction of Electronic Transformer Error Based on Intelligent AlgorithmsYuanyu Ye0Aichao Yang1Yu Wu2Chen Hu3Min Li4Yan Li5Xiaosong Deng6State Grid Jiangxi Electric Power Company, Nanchang 330077, ChinaState Grid Jiangxi Electric Power Company, Nanchang 330077, ChinaState Grid Jiangxi Electric Power Company, Nanchang 330077, ChinaState Grid Jiangxi Electric Power Company, Nanchang 330077, ChinaState Grid Jiangxi Electric Power Company, Nanchang 330077, ChinaThe State Key Laboratory of Power Transmission Equipment & System Security and New Technology, Chongqing University, Chongqing 400044, ChinaThe State Key Laboratory of Power Transmission Equipment & System Security and New Technology, Chongqing University, Chongqing 400044, ChinaAs 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.http://dx.doi.org/10.1155/2020/9867985
collection DOAJ
language English
format Article
sources DOAJ
author Yuanyu Ye
Aichao Yang
Yu Wu
Chen Hu
Min Li
Yan Li
Xiaosong Deng
spellingShingle Yuanyu Ye
Aichao Yang
Yu Wu
Chen Hu
Min Li
Yan Li
Xiaosong Deng
Short-Term Prediction of Electronic Transformer Error Based on Intelligent Algorithms
Journal of Control Science and Engineering
author_facet Yuanyu Ye
Aichao Yang
Yu Wu
Chen Hu
Min Li
Yan Li
Xiaosong Deng
author_sort Yuanyu Ye
title Short-Term Prediction of Electronic Transformer Error Based on Intelligent Algorithms
title_short Short-Term Prediction of Electronic Transformer Error Based on Intelligent Algorithms
title_full Short-Term Prediction of Electronic Transformer Error Based on Intelligent Algorithms
title_fullStr Short-Term Prediction of Electronic Transformer Error Based on Intelligent Algorithms
title_full_unstemmed Short-Term Prediction of Electronic Transformer Error Based on Intelligent Algorithms
title_sort short-term prediction of electronic transformer error based on intelligent algorithms
publisher Hindawi Limited
series Journal of Control Science and Engineering
issn 1687-5249
1687-5257
publishDate 2020-01-01
description 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.
url http://dx.doi.org/10.1155/2020/9867985
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