Simplified Identification Strategy of Load Model Based on Global Sensitivity Analysis

Load modeling plays an important role in accessing and enhancing the dynamic stability of power systems. Though the Synthesis Load Model Considering Voltage Regulation of Distribution Network (<inline-formula> <tex-math notation="LaTeX">$\pi $ </tex-math></inline-formu...

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Autores principales:	Xin Tian, Xueliang Lii, Long Zhao, Zuoyun Tan, Shuchen Luo, Canbing Li
Formato:	article
Lenguaje:	EN
Publicado:	IEEE 2020
Materias:	Load modeling synthesis load model sensitivity analysis global sensitivity analysis Electrical engineering. Electronics. Nuclear engineering TK1-9971
Acceso en línea:	https://doaj.org/article/81fc8e91cbc54776bc9bf5bb9d715312
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spelling	oai:doaj.org-article:81fc8e91cbc54776bc9bf5bb9d7153122021-11-19T00:05:13ZSimplified Identification Strategy of Load Model Based on Global Sensitivity Analysis2169-353610.1109/ACCESS.2020.3007639https://doaj.org/article/81fc8e91cbc54776bc9bf5bb9d7153122020-01-01T00:00:00Zhttps://ieeexplore.ieee.org/document/9134728/https://doaj.org/toc/2169-3536Load modeling plays an important role in accessing and enhancing the dynamic stability of power systems. Though the Synthesis Load Model Considering Voltage Regulation of Distribution Network (<inline-formula> <tex-math notation="LaTeX">$\pi $ </tex-math></inline-formula> model) has high accuracy, its parameters are too many. In order to improve the identification efficiency and reduce the difficulty of identification, a simplified model identification strategy based on parameter sensitivity analysis is proposed. Firstly, based on the global sensitivity analysis, the sensitivity analysis of the model parameters is carried out to obtain the First Order Sensitivity Indices (<italic>FSI</italic>)and Total Sensitivity Indices (<italic>TSI</italic>). Secondly, the <italic>FSI</italic> and <italic>TSI</italic> of each parameter are analyzed, and the effect on the output of model of each parameter is determined by <italic>FSI</italic>. For less influential parameters, whether the parameter should be fixed as constant is determined by the value of <italic>TSI</italic>. The parameter whose <italic>TSI</italic> equal or approximately equal to zero should be fixed as a constant. Finally, the improved genetic algorithm is used to identify the parameter-simplified model, and the effectiveness of the simplified identification strategy is verified by comparing the fitting effects with the measured curve and the residual and the integrated parameter models.Xin TianXueliang LiiLong ZhaoZuoyun TanShuchen LuoCanbing LiIEEEarticleLoad modelingsynthesis load modelsensitivity analysisglobal sensitivity analysisElectrical engineering. Electronics. Nuclear engineeringTK1-9971ENIEEE Access, Vol 8, Pp 131545-131552 (2020)
institution	DOAJ
collection	DOAJ
language	EN
topic	Load modeling synthesis load model sensitivity analysis global sensitivity analysis Electrical engineering. Electronics. Nuclear engineering TK1-9971
spellingShingle	Load modeling synthesis load model sensitivity analysis global sensitivity analysis Electrical engineering. Electronics. Nuclear engineering TK1-9971 Xin Tian Xueliang Lii Long Zhao Zuoyun Tan Shuchen Luo Canbing Li Simplified Identification Strategy of Load Model Based on Global Sensitivity Analysis
description	Load modeling plays an important role in accessing and enhancing the dynamic stability of power systems. Though the Synthesis Load Model Considering Voltage Regulation of Distribution Network (<inline-formula> <tex-math notation="LaTeX">$\pi $ </tex-math></inline-formula> model) has high accuracy, its parameters are too many. In order to improve the identification efficiency and reduce the difficulty of identification, a simplified model identification strategy based on parameter sensitivity analysis is proposed. Firstly, based on the global sensitivity analysis, the sensitivity analysis of the model parameters is carried out to obtain the First Order Sensitivity Indices (<italic>FSI</italic>)and Total Sensitivity Indices (<italic>TSI</italic>). Secondly, the <italic>FSI</italic> and <italic>TSI</italic> of each parameter are analyzed, and the effect on the output of model of each parameter is determined by <italic>FSI</italic>. For less influential parameters, whether the parameter should be fixed as constant is determined by the value of <italic>TSI</italic>. The parameter whose <italic>TSI</italic> equal or approximately equal to zero should be fixed as a constant. Finally, the improved genetic algorithm is used to identify the parameter-simplified model, and the effectiveness of the simplified identification strategy is verified by comparing the fitting effects with the measured curve and the residual and the integrated parameter models.
format	article
author	Xin Tian Xueliang Lii Long Zhao Zuoyun Tan Shuchen Luo Canbing Li
author_facet	Xin Tian Xueliang Lii Long Zhao Zuoyun Tan Shuchen Luo Canbing Li
author_sort	Xin Tian
title	Simplified Identification Strategy of Load Model Based on Global Sensitivity Analysis
title_short	Simplified Identification Strategy of Load Model Based on Global Sensitivity Analysis
title_full	Simplified Identification Strategy of Load Model Based on Global Sensitivity Analysis
title_fullStr	Simplified Identification Strategy of Load Model Based on Global Sensitivity Analysis
title_full_unstemmed	Simplified Identification Strategy of Load Model Based on Global Sensitivity Analysis
title_sort	simplified identification strategy of load model based on global sensitivity analysis
publisher	IEEE
publishDate	2020
url	https://doaj.org/article/81fc8e91cbc54776bc9bf5bb9d715312
work_keys_str_mv	AT xintian simplifiedidentificationstrategyofloadmodelbasedonglobalsensitivityanalysis AT xuelianglii simplifiedidentificationstrategyofloadmodelbasedonglobalsensitivityanalysis AT longzhao simplifiedidentificationstrategyofloadmodelbasedonglobalsensitivityanalysis AT zuoyuntan simplifiedidentificationstrategyofloadmodelbasedonglobalsensitivityanalysis AT shuchenluo simplifiedidentificationstrategyofloadmodelbasedonglobalsensitivityanalysis AT canbingli simplifiedidentificationstrategyofloadmodelbasedonglobalsensitivityanalysis
_version_	1718420677030051840

Simplified Identification Strategy of Load Model Based on Global Sensitivity Analysis

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