全局优化算法在Preisach磁滞模型参数辨识问题中的应用与性能对比

doi:10.19595/j.cnki.1000-6753.tces.201294

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Abstract The rapid identification of the Preisach model parameters is of great significance to realize the finite element calculation of electrical equipment considering the hysteresis characteristics. Combined the explicit Everett function with the Preisach model, this paper proposes a parameter identification method based on an improved velocity-controlled particle swarm optimization algorithm, and compares the efficiency of the global optimization algorithm. Firstly, a parameterized explicit expression of the Everett function is constructed to solve the problem that low computational efficiency of the traditional discrete Preisach model as the storage of Everett matrix is huge. Secondly, a parameter identification method of the Preisach model based on the improved velocity-controlled particle swarm optimization algorithm is proposed. Based on the measured quasi-static hysteresis loops of silicon steel sheet, the parameters of the Preisach model are identified. Finally, the simulated annealing algorithm, genetic algorithm, and the algorithm proposed in this paper are compared and analyzed in terms of the iterations number and calculation time, the accuracy of hysteresis loop simulation, and the success rate of Parameter identification. The results show that the improved velocity-controlled particle swarm optimization algorithm proposed in this paper has high identification accuracy, fast convergence speed, and high success rate in identifying the Preisach model.

Key words： Hysteresis model global optimization electrical magnetic material magnetic properties simulation

Received: 23 September 2020

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TM271

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	Chen Long
	Yi Qiongyang
	Ben Tong
	Zhang Zeyu
	Wang Youhua

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Chen Long,Yi Qiongyang,Ben Tong等. Application and Performance Comparison of Global Optimization Algorithms in the Parameter Identification Problems of the Preisach Hysteresis Model[J]. Transactions of China Electrotechnical Society, 2021, 36(12): 2585-2593.

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https://dgjsxb.ces-transaction.com/EN/10.19595/j.cnki.1000-6753.tces.201294 OR https://dgjsxb.ces-transaction.com/EN/Y2021/V36/I12/2585

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