Black-box models for liquid nitrogen arc and its parameters optimization by PSO algorithm

Author:

Junaid MuhammadORCID,Cao Shuzhi,Yu Wenqing,Yu Xiaolong,Yu Dongsheng,Wang Jianhua

Abstract

Abstract Sulphur Hexafluoride (SF6) has been widely utilized in the Gas Insulated Switchgear (GIS) due to its great insulation ability. However, SF6 has great greenhouse effect. Liquid nitrogen (LN2) has been considered as a promising substitute for the SF6 gas because of its good insulation, arc quenching and cost effectiveness. To accurately simulate the LN2 switch, it is essential to establish a mathematical model for the LN2 arc. Black-box models have been commonly used in describing the dynamic characteristics of gas arc in circuit breaker simulations. There are numerous types of black-box models for gas arc, yet there is no literature available about the application of black-box model for the LN2 arc. This paper aims to establish the black-box model of LN2 arc. Based on the experimental data, several kinds of black-box models including Mayr, Cassie, Schwarz, Habedank and TP KEMA models were established, and their parameters were optimized by the Particle Swarm Optimization (PSO) algorithm. The performance of these black-box models was evaluated by the conductance error. The results indicated that black-box models can be employed for LN2 arc simulations, and the TP KEMA model exhibits the best performance with minimal conductance errors throughout the entire arcing process.

Funder

National Natural Science Foundation of China, “Research Fund for International Young Scientist (RFIS-1)”

2021 Jiangsu “Shuang-Chuang Doctor (Mass Innovation and Entrepreneurship) Talent Program”

State Key Laboratory of Electrical Insulation and Power Equipment

Publisher

IOP Publishing

Subject

Condensed Matter Physics,Mathematical Physics,Atomic and Molecular Physics, and Optics

Cited by 1 articles. 订阅此论文施引文献 订阅此论文施引文献,注册后可以免费订阅5篇论文的施引文献,订阅后可以查看论文全部施引文献

1. Parameter Optimization of TP KEMA model for Liquid Nitrogen arc by Heuristics Algorithms;2023 IEEE International Conference on Applied Superconductivity and Electromagnetic Devices (ASEMD);2023-10-27

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