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Parameter Identification of J-A Model Based on Improved Algorithm (GSS-PSO)

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Application of Intelligent Systems in Multi-modal Information Analytics (MMIA 2021)

Part of the book series: Advances in Intelligent Systems and Computing ((AISC,volume 1385))

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Abstract

When the Jiles-Atherton hysteresis model is applied to analyze the B-H curve of current transformer, it is necessary to accurately identify the five key arguments in the Jiles-Atherton model. Since the existing identification methods have the problems of long calculation time and poor optimization ability, an improved PSO algorithm is proposed to recognize the key parameters of Jiles-Atherton model. With the genetic selection strategy is recommended into the PSO algorithm, the global search ability of the algorithm is improved by increasing the diversity of the PSO, so as to improve the accuracy of the authentication of the key parameters of Jiles-Atherton model. In this paper, the calculation speed and accuracy of the proposed improved algorithm (gss-pso) are compared with other intelligent algorithms in identifying the key parameters of Jiles-Atherton model. The sequelae show that the error of the B-H curve calculated by the improved algorithm is the least, and the identification efficiency is higher, which proves the accuracy and efficacy of the algorithm in parameter authentication of Jiles-Atherton hysteresis model.

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Acknowledgements

This work is supported by the research project of operational quality evaluation model of metering transformers for real power simulation (52094018002A).

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Authors and Affiliations

  1. State Grid Shanghai Electric Research Institute, Shanghai, 200052, China

    Haibin Chen, Yi Cao, Bing Lu & Liwen Chen

  2. China Electric Power Research Institute, Wuhan, 430074, Hubei, China

    Min Lei

  3. East China Grid Corporation, Shanghai, 200120, China

    Lu Wang

Authors
  1. Haibin Chen
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  2. Lu Wang
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  3. Min Lei
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  4. Yi Cao
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  5. Bing Lu
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  6. Liwen Chen
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Editor information

Editors and Affiliations

  1. Decision and Information Sciences, Oakland University, Rochester, MI, USA

    Vijayan Sugumaran

  2. Shanghai University, Shanghai, China

    Zheng Xu

  3. Department of Informatics, University of Leicester, Leicester, Leicestershire, UK

    Huiyu Zhou

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Chen, H., Wang, L., Lei, M., Cao, Y., Lu, B., Chen, L. (2021). Parameter Identification of J-A Model Based on Improved Algorithm (GSS-PSO). In: Sugumaran, V., Xu, Z., Zhou, H. (eds) Application of Intelligent Systems in Multi-modal Information Analytics. MMIA 2021. Advances in Intelligent Systems and Computing, vol 1385. Springer, Cham. https://doi.org/10.1007/978-3-030-74814-2_115

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