基于改进鲸鱼算法的电流互感器J-A模型磁滞参数识别

基于J-A磁滞模型模拟电流互感器铁芯磁滞特性的关键是模型参数的精准快速识别。针对现有J-A模型参数提取时输入输出均不易测量和提取方法收敛速度慢、精度低等问题，提出了一种基于改进鲸鱼算法的ψ-i J-A磁滞模型的参数提取方法。该模型以磁链和电流为基础，而不是以磁通密度和磁场为基础。采用加入自适应权重值调整和搜索策略的改进鲸鱼算法对参数进行提取。用所提方法和粒子群算法、鲸鱼算法分别识别P型电流互感器和PR型电流互感器的磁滞曲线，对比可得改进鲸鱼算法精度更高、迭代次数更少，验证了所提方法的高效性。

Hysteresis Parameter Identification of J-A Model Current Transformer Based on Improved Whale Algorithm

The accurate and rapid identification of model parameters is crucial to the simulation of the hysteresis characteristics of current transformer cores based on the J-A hysteresis model.With regard to the difficulties in measuring the input and output of the existing J-A model parameters in addition to the slow convergence speed and low accuracy of the extraction method,a parameter extraction method of theψ-i JA hysteresis model based on the improved whale algorithm is proposed in this paper.The model is established in terms of flux linkage and current instead of flux density and magnetic field.The parameters are extracted using an improved whale algorithm with adaptive adjustment of weights and search strategies.By taking advantage of the proposed method,particle swarm algorithm,and whale algorithm respectively,the hysteresis curves of P-type current transformer and PR-type current transformer are identified and compared.The result comparison shows that the improved whale algorithm has higher accuracy and fewer iterations,such that the high efficiency of the proposed method is verified.