中国电机工程学报2024，Vol.44Issue(5)：2047-2057,中插33,12.DOI:10.13334/j.0258-8013.pcsee.222188
多目标差分进化算法改进与电工钢片磁致伸缩模型参数辨识
Improvement of Multi-objective Differential Evolution Algorithm and Parameters Identification of Magnetostriction Model of Electrical Steel Sheet
摘要
Abstract
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%.关键词
电工钢片/磁致伸缩模型/参数辨识/多目标差分进化算法/Jiles-Atherton-Sablik模型/Energetic磁滞模型Key words
electrical steel sheet/magnetostriction model/parameters identification/multi-objective differential evolution algorithm/Jiles-Atherton-Sablik model/Energetic hysteresis model分类
信息技术与安全科学引用本文复制引用
陈昊,李琳,王亚琦,刘洋..多目标差分进化算法改进与电工钢片磁致伸缩模型参数辨识[J].中国电机工程学报,2024,44(5):2047-2057,中插33,12.基金项目
国家重点研发计划项目(2021YFB2401703) （2021YFB2401703）
国家自然科学基金项目(52177005). National Key R&D Program of China(2021YFB2401703) （52177005）
Project Supported by National Natural Science Foundation of China(52177005). （52177005）
