Comparison of algorithms for predicting plasma physical parameters of SF6–Cu mixtures at local thermodynamic equilibrium state via machine learning

In order to study the arc process of the SF6 circuit breaker during the current breaking process, it is necessary to calculate the physical parameters of the arc discharge plasma. However, the calculation of plasma physical parameters is very difficult and complicated and generally requires solving...

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Autores principales:	Can Ding, Qingchang Ding, Zhenyi Wang, Yiyuan Zhou, Chen Chen
Formato:	article
Lenguaje:	EN
Publicado:	AIP Publishing LLC 2021
Materias:	Physics QC1-999
Acceso en línea:	https://doaj.org/article/754e5c19dea54d9cb60d6ba188045df0
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spelling	oai:doaj.org-article:754e5c19dea54d9cb60d6ba188045df02021-12-01T18:52:06ZComparison of algorithms for predicting plasma physical parameters of SF6–Cu mixtures at local thermodynamic equilibrium state via machine learning2158-322610.1063/5.0061514https://doaj.org/article/754e5c19dea54d9cb60d6ba188045df02021-11-01T00:00:00Zhttp://dx.doi.org/10.1063/5.0061514https://doaj.org/toc/2158-3226In order to study the arc process of the SF6 circuit breaker during the current breaking process, it is necessary to calculate the physical parameters of the arc discharge plasma. However, the calculation of plasma physical parameters is very difficult and complicated and generally requires solving dozens of differential equations. Based on the machine learning method, this paper constructs a learning prediction model of physical property parameters in a local thermodynamic equilibrium state without solving a large number of differential equations so as to perform a rapid prediction of physical property parameters in other scenarios based on the existing physical parameter database. This paper uses the support vector machine, K-nearest neighbor algorithm, gradient boosting regression, decision tree, and random forest algorithm to predict and calculate the thermodynamic parameters and transport characteristics of SF6 at different pressures and temperatures. At the same time, this paper also predicts and calculates the parameters of the SF6–Cu mixed gas at different mixed concentrations. The results show that the machine learning algorithm can predict and generate consistent gas property parameter data, indicating that the model has good generalization performance. Finally, by comparing the error measures of the prediction results of various algorithms, the algorithm suitable for predicting the physical parameters is found to improve the prediction accuracy.Can DingQingchang DingZhenyi WangYiyuan ZhouChen ChenAIP Publishing LLCarticlePhysicsQC1-999ENAIP Advances, Vol 11, Iss 11, Pp 115102-115102-6 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	Physics QC1-999
spellingShingle	Physics QC1-999 Can Ding Qingchang Ding Zhenyi Wang Yiyuan Zhou Chen Chen Comparison of algorithms for predicting plasma physical parameters of SF6–Cu mixtures at local thermodynamic equilibrium state via machine learning
description	In order to study the arc process of the SF6 circuit breaker during the current breaking process, it is necessary to calculate the physical parameters of the arc discharge plasma. However, the calculation of plasma physical parameters is very difficult and complicated and generally requires solving dozens of differential equations. Based on the machine learning method, this paper constructs a learning prediction model of physical property parameters in a local thermodynamic equilibrium state without solving a large number of differential equations so as to perform a rapid prediction of physical property parameters in other scenarios based on the existing physical parameter database. This paper uses the support vector machine, K-nearest neighbor algorithm, gradient boosting regression, decision tree, and random forest algorithm to predict and calculate the thermodynamic parameters and transport characteristics of SF6 at different pressures and temperatures. At the same time, this paper also predicts and calculates the parameters of the SF6–Cu mixed gas at different mixed concentrations. The results show that the machine learning algorithm can predict and generate consistent gas property parameter data, indicating that the model has good generalization performance. Finally, by comparing the error measures of the prediction results of various algorithms, the algorithm suitable for predicting the physical parameters is found to improve the prediction accuracy.
format	article
author	Can Ding Qingchang Ding Zhenyi Wang Yiyuan Zhou Chen Chen
author_facet	Can Ding Qingchang Ding Zhenyi Wang Yiyuan Zhou Chen Chen
author_sort	Can Ding
title	Comparison of algorithms for predicting plasma physical parameters of SF6–Cu mixtures at local thermodynamic equilibrium state via machine learning
title_short	Comparison of algorithms for predicting plasma physical parameters of SF6–Cu mixtures at local thermodynamic equilibrium state via machine learning
title_full	Comparison of algorithms for predicting plasma physical parameters of SF6–Cu mixtures at local thermodynamic equilibrium state via machine learning
title_fullStr	Comparison of algorithms for predicting plasma physical parameters of SF6–Cu mixtures at local thermodynamic equilibrium state via machine learning
title_full_unstemmed	Comparison of algorithms for predicting plasma physical parameters of SF6–Cu mixtures at local thermodynamic equilibrium state via machine learning
title_sort	comparison of algorithms for predicting plasma physical parameters of sf6–cu mixtures at local thermodynamic equilibrium state via machine learning
publisher	AIP Publishing LLC
publishDate	2021
url	https://doaj.org/article/754e5c19dea54d9cb60d6ba188045df0
work_keys_str_mv	AT canding comparisonofalgorithmsforpredictingplasmaphysicalparametersofsf6cumixturesatlocalthermodynamicequilibriumstateviamachinelearning AT qingchangding comparisonofalgorithmsforpredictingplasmaphysicalparametersofsf6cumixturesatlocalthermodynamicequilibriumstateviamachinelearning AT zhenyiwang comparisonofalgorithmsforpredictingplasmaphysicalparametersofsf6cumixturesatlocalthermodynamicequilibriumstateviamachinelearning AT yiyuanzhou comparisonofalgorithmsforpredictingplasmaphysicalparametersofsf6cumixturesatlocalthermodynamicequilibriumstateviamachinelearning AT chenchen comparisonofalgorithmsforpredictingplasmaphysicalparametersofsf6cumixturesatlocalthermodynamicequilibriumstateviamachinelearning
_version_	1718404699054407680

Comparison of algorithms for predicting plasma physical parameters of SF6–Cu mixtures at local thermodynamic equilibrium state via machine learning

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