CVT measurement error correction by double regression-based particle swarm optimization compensation algorithm

The capacitive voltage transformer (CVT) is a widely-used voltage measuring instrument directly related to the safety and security of the power system operation. As the CVT’s range of application scenarios is getting wider, its environmental adaptability requirements become more stringent. Therefore...

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Autores principales:	Feng Zhou, Jicheng Yu, Peng Zhao, Changxi Yue, Siyuan Liang, He Li
Formato:	article
Lenguaje:	EN
Publicado:	Elsevier 2021
Materias:	Capacitive voltage transformer Measurement error correction Nonlinear regression Particle swarm optimization Electrical engineering. Electronics. Nuclear engineering TK1-9971
Acceso en línea:	https://doaj.org/article/3a3df4c185054904bf894db57b06f0a4
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spelling	oai:doaj.org-article:3a3df4c185054904bf894db57b06f0a42021-11-26T04:33:43ZCVT measurement error correction by double regression-based particle swarm optimization compensation algorithm2352-484710.1016/j.egyr.2021.08.056https://doaj.org/article/3a3df4c185054904bf894db57b06f0a42021-11-01T00:00:00Zhttp://www.sciencedirect.com/science/article/pii/S2352484721006600https://doaj.org/toc/2352-4847The capacitive voltage transformer (CVT) is a widely-used voltage measuring instrument directly related to the safety and security of the power system operation. As the CVT’s range of application scenarios is getting wider, its environmental adaptability requirements become more stringent. Therefore, the CVT measurement error generated from the atmospheric environment is becoming a matter. This paper proposes a CVT measurement error correction method by employing a double regression-based particle swarm optimization compensation algorithm (DR-PSO). In DR-PSO, the double regression algorithm is used to calculate the rough result, and the theoretical formula is used to calculate the multi-type disturbance. Finally, the particle swarm optimization is used to determine the optimal influence factor matrix and obtain accurate results. Based on the transformer’s multidimensional operating data, the DR-PSO can be trained by the empirical data, and then the algorithm shall be used to correct CVT errors based on the real-time data. The test by actual data of State Grid proved that the root mean squared error of the proposed algorithm’s phase error was only 0.13, and the mean absolute error of the amplitude error is only 0.0009. Compared with other algorithms such as the equal weight method, information entropy method, and multiple regression method, the proposed algorithm can calculate the measurement error with the highest accuracy.Feng ZhouJicheng YuPeng ZhaoChangxi YueSiyuan LiangHe LiElsevierarticleCapacitive voltage transformerMeasurement error correctionNonlinear regressionParticle swarm optimizationElectrical engineering. Electronics. Nuclear engineeringTK1-9971ENEnergy Reports, Vol 7, Iss , Pp 191-200 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	Capacitive voltage transformer Measurement error correction Nonlinear regression Particle swarm optimization Electrical engineering. Electronics. Nuclear engineering TK1-9971
spellingShingle	Capacitive voltage transformer Measurement error correction Nonlinear regression Particle swarm optimization Electrical engineering. Electronics. Nuclear engineering TK1-9971 Feng Zhou Jicheng Yu Peng Zhao Changxi Yue Siyuan Liang He Li CVT measurement error correction by double regression-based particle swarm optimization compensation algorithm
description	The capacitive voltage transformer (CVT) is a widely-used voltage measuring instrument directly related to the safety and security of the power system operation. As the CVT’s range of application scenarios is getting wider, its environmental adaptability requirements become more stringent. Therefore, the CVT measurement error generated from the atmospheric environment is becoming a matter. This paper proposes a CVT measurement error correction method by employing a double regression-based particle swarm optimization compensation algorithm (DR-PSO). In DR-PSO, the double regression algorithm is used to calculate the rough result, and the theoretical formula is used to calculate the multi-type disturbance. Finally, the particle swarm optimization is used to determine the optimal influence factor matrix and obtain accurate results. Based on the transformer’s multidimensional operating data, the DR-PSO can be trained by the empirical data, and then the algorithm shall be used to correct CVT errors based on the real-time data. The test by actual data of State Grid proved that the root mean squared error of the proposed algorithm’s phase error was only 0.13, and the mean absolute error of the amplitude error is only 0.0009. Compared with other algorithms such as the equal weight method, information entropy method, and multiple regression method, the proposed algorithm can calculate the measurement error with the highest accuracy.
format	article
author	Feng Zhou Jicheng Yu Peng Zhao Changxi Yue Siyuan Liang He Li
author_facet	Feng Zhou Jicheng Yu Peng Zhao Changxi Yue Siyuan Liang He Li
author_sort	Feng Zhou
title	CVT measurement error correction by double regression-based particle swarm optimization compensation algorithm
title_short	CVT measurement error correction by double regression-based particle swarm optimization compensation algorithm
title_full	CVT measurement error correction by double regression-based particle swarm optimization compensation algorithm
title_fullStr	CVT measurement error correction by double regression-based particle swarm optimization compensation algorithm
title_full_unstemmed	CVT measurement error correction by double regression-based particle swarm optimization compensation algorithm
title_sort	cvt measurement error correction by double regression-based particle swarm optimization compensation algorithm
publisher	Elsevier
publishDate	2021
url	https://doaj.org/article/3a3df4c185054904bf894db57b06f0a4
work_keys_str_mv	AT fengzhou cvtmeasurementerrorcorrectionbydoubleregressionbasedparticleswarmoptimizationcompensationalgorithm AT jichengyu cvtmeasurementerrorcorrectionbydoubleregressionbasedparticleswarmoptimizationcompensationalgorithm AT pengzhao cvtmeasurementerrorcorrectionbydoubleregressionbasedparticleswarmoptimizationcompensationalgorithm AT changxiyue cvtmeasurementerrorcorrectionbydoubleregressionbasedparticleswarmoptimizationcompensationalgorithm AT siyuanliang cvtmeasurementerrorcorrectionbydoubleregressionbasedparticleswarmoptimizationcompensationalgorithm AT heli cvtmeasurementerrorcorrectionbydoubleregressionbasedparticleswarmoptimizationcompensationalgorithm
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CVT measurement error correction by double regression-based particle swarm optimization compensation algorithm

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