摘要
准确且高效地辨识电工钢片磁致伸缩模型参数是模型在变压器铁心振动分析中的应用前提。针对现有单目标优化算法不能兼顾参数辨识精度和速度的问题,该文基于改进Jiles-Atherton-Sablik和Energetic模型相结合的磁致伸缩模型,将该模型的参数辨识转换为多目标优化问题。以磁滞回线和磁致伸缩曲线的均方根误差作为待优化的2个目标,建立参数辨识的多目标优化数学模型。基于该模型,从控制参数自适应技术、变异算子改进策略以及选择算子改进策略3个方面对多目标差分进化算法进行改进,从而提出一种采用改进多目标差分进化算法的磁致伸缩模型参数辨识方法。通过与现有方法对比,该文方法的磁滞回线求解精度提升17.84%,磁致伸缩曲线求解精度提升13.60%,辨识速度提升41.57%。
Accurate and efficient identification of the magnetostriction model parameters of electrical steel sheet is the premise of the model's application in the vibration analysis of the transformer core.Aiming at the problem that the existing parameters identification method based on the single-objective optimization algorithm cannot take into account the accuracy and speed,in this paper,based on the magnetostriction model of combining the improved Jiles-Atherton-Sablik and Energetic models,the parameters identification of the model is transformed into a multi-objective optimization problem.Taking the root mean square error of hysteresis loop and magnetostriction curve as two optimization objectives,a multi-objective optimization mathematical model for parameters identification is established.Based on this model,the multi-objective differential evolution algorithm is improved from three aspects:control parameters adaptation technology,mutation operator improvement strategy and selection operator improvement strategy;thus a parameters identification method of magnetostriction model by using the improved multi-objective differential evolution algorithm is proposed.Compared with the existing method,the solution accuracy of hysteresis loop of the proposed method is improved by 17.84%,the solution accuracy of magnetostriction curve is improved by 13.60%,and the identification speed is improved by 41.57%.
作者
陈昊
李琳
王亚琦
刘洋
CHEN Hao;LI Lin;WANG Yaqi;LIU Yang(State Key Laboratory of Alternate Electrical Power System with Renewable Energy Sources(North China Electric Power University),Changping District,Beijing 102206,China;State Key Laboratory of Advanced Power Transmission Technology(State Grid Smart Grid Research Institute Co.,Ltd.),Changping District,Beijing 102209,China)
出处
《中国电机工程学报》
EI
CSCD
北大核心
2024年第5期2047-2057,I0033,共12页
Proceedings of the CSEE
基金
国家重点研发计划项目(2021YFB2401703)
国家自然科学基金项目(52177005)。
