Parameter Extraction for Jiles-Atherton Hysteresis Model Based on the Hybrid Technique of Stochastic and Deterministic Optimization Algorithm
Liu Ren1, Li Lin1, Wang Yaqi1, Han Yu2, Liu Yang2
1. State Key Laboratory of Alternate Electrical Power System with Renewable Energy SourcesNorth China Electric Power University Beijing 102206 China; 2. Global Energy Interconnection Research Institute Beijing 102211 China
Abstract：The primary task of using J-A hysteresis model to simulate the hysteresis phenomenons of ferromagnetic materials is the model parameters' efficient identification. Owing to the slow convergence rate and low accuracy level of existing parameter extraction methods, a hybrid technique that couples simulated annealing (SA) with Levenberg-Marquardt (L-M) is proposed. This algorithm combines the merits of strong global search ability in SA and fast local convergence speed in L-M. In the initial iteration process, SA is used to quickly lock the optimized region. Then according to the introduced switching criterion, SA stops working, and its current solution is transferred to the L-M. Aiming at the problem of ill-conditioned matrix that appears in the process of parameter extraction when using traditional L-M, the sensitivity function matrix is normalized so as to obtain the normalized L-M, which is suitable for the rapid parameter identification of J-A model parameters. After receiving the solution provided by SA, the normalized L-M takes this values as its initial parameters for local search. The simulation and experimental results show that the proposed hybrid algorithm has the advantages of fast convergence and high accuracy at the same time.
刘任, 李琳, 王亚琦, 韩钰, 刘洋. 基于随机性与确定性混合优化算法的Jiles-Atherton磁滞模型参数提取[J]. 电工技术学报, 2019, 34(11): 2260-2268.
Liu Ren, Li Lin, Wang Yaqi, Han Yu, Liu Yang. Parameter Extraction for Jiles-Atherton Hysteresis Model Based on the Hybrid Technique of Stochastic and Deterministic Optimization Algorithm. Transactions of China Electrotechnical Society, 2019, 34(11): 2260-2268.
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2019, Vol. 34 
