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作 者:曹永娟[1] 毛瑞 冯亮亮 李康 CAO Yong-juan;MAO Rui;FENG Liang-liang;LI Kang(School of Automation, Nanjing University of Information Science & Technology, Nanjing 210044, China;Jiangsu Provincial Collaborative Center for Atmospheric Environment and Equipment Technology, Nanjing 210044, China)
机构地区:[1]南京信息工程大学自动化学院,江苏南京210044 [2]江苏省大气环境与装备技术协同中心,江苏南京210044
出 处:《电工电能新技术》2022年第5期26-34,共9页Advanced Technology of Electrical Engineering and Energy
基 金:国家自然科学基金资助项目(51507082)。
摘 要:针对永磁同步电机利用元启发式算法解决参数辨识问题时,算法在迭代后期容易陷入局部最优、辨识精度不高、迭代次数过多等问题,本文提出了一种融合差分进化算法中的交叉变异策略以及加入动态搜索的麻雀搜索算法。该算法在基本麻雀搜索算法的基础上,在麻雀发现者的位置更新阶段引入交叉变异策略和动态搜索,使得算法在前期增加了种群的多样性,避免了陷入局部最优的情况。通过交叉变异策略和动态搜索,在保证收敛精度的同时,大大提高了算法的迭代速度。在基于电机电压方程建立的非线性数学模型的基础上,参数辨识模型只需测量获得永磁同步电机的电压、电流及角速度等信息,再将适应度函数通过改进的新算法在辨识模型中得到辨识结果。经仿真及实物验证,该算法可以实现对永磁同步电机参数进行快速和精准的辨识。The meta heuristic algorithm for identifying permanent magnet synchronous motor has some problems in the late iteration,such as falling into local optimization easily,low identification accuracy and too much iterations.In order to solve above problems,a novel sparrow search algorithm which combines the cross mutation strategy of differential evolution algorithm and dynamic search algorithm is proposed in this paper.Based on the basic sparrow search algorithm,this algorithm introduces a cross mutation strategy and dynamic search in the location update phase of the sparrow finder,which makes the algorithm increase the diversity of the population in the early stage and avoid falling into the local optimal situation.Through the cross-mutation strategy and dynamic search,the iteration speed of the algorithm is greatly improved while ensuring the accuracy of convergence.On the basis of the nonlinear mathematical model established by the motor voltage equation,the parameter identification model only needs to measure the voltage,current and angular velocity of the PMSM.Then the improved new algorithm is used to obtain the identification results in the identification model through the fitness function.Simulation and physical verification show that the algorithm can realize rapid and accurate identification of PMSM parameters.
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