基于改进秃鹰搜索算法的变压器J-A模型参数辨识

Parameter identification of transformer J-A model based on improved BES algorithm

在对变压器的精确建模中,铁磁材料特性的表示是一大关键点。J-A磁滞模型具有参数较少、物理意义清晰等优点,在研究中得到广泛应用。针对现有J-A模型参数辨识所使用的优化算法存在精度低、耗时久等问题,本文提出了一种基于改进秃鹰搜索算法,以实现对J-A磁滞模型参数的快速、精确辨识。该方法融合混沌映射、精英反向学习及自适应权重,能使算法具有很好的全局搜索能力与局部搜索能力,将待求值快速收敛于全局最优值。本文使用该算法对磁滞曲线的J-A磁滞模型参数进行辨识,然后基于所得到的参数对磁性材料的磁滞特性进行模拟,并将模拟结果与其他算法的模拟结果进行了对比分析。结果表明,使用该算法辨识模型参数时收敛速度更快、辨识精度更高,且基于该算法所辨识的参数模拟磁滞回线与实测曲线吻合较好,验证了该算法的实用性、有效性与优越性。

The representation of ferromagnetic material properties is a key point in the accurate modeling of transformers.J-A hysteresis model has the advantages of fewer parameters and clear physical significance,and has been widely used in research.Aiming at the problems of low accuracy and time consuming of the optimization algorithms used in parameter identification of the J-A model,an improved condor search algorithm is proposed in this paper to achieve fast and accurate identification of parameters of the J-A hysteresis model.This method combines chaos mapping,elite reverse learning and adaptive weight,which makes the algorithm have good global search ability and local search ability,and can converge to the global optimal value quickly at the same time.Combined with the experimental data,this paper uses this algorithm to identify the parameters of the J-A hysteresis model,and then simulates the hysteresis characteristics of magnetic materials based on the obtained parameters,and compares the simulation results with those of other algorithms.The results show that the proposed algorithm has faster convergence speed and higher identification accuracy when identifying model parameters.The simulated hysteresis loop based on the proposed algorithm is in good agreement with the measured curve,which verifies the practicability and effectiveness of the proposed algorithm.