改进细菌觅食算法的永磁同步电机参数辨识

Parameter identification of permanent magnet synchronous motor based on modified bacterial foraging algorithm

应用群智能算法对永磁同步电机(PMSM)进行参数辨识后期容易进入局部最优,从而导致辨识误差大,为此提出一种融合小生境技术的改进细菌觅食算法(MBFA)。通过构建目标追踪函数,利用电机电流、电压和转速等直接测量的信号实现对电机d轴电感、q轴电感、定子电阻和永磁体磁链的快速、准确辨识;辨识过程中通过引入格型准则对目标解空间进行拟蒙特卡罗采样提高算法的全局搜索能力;基于小生境技术进行在线多种群协同搜索策略提高算法的搜索效率和寻优精度;最后通过引入一种种群实时监测和动态更新机制保证了算法在整个寻优过程的鲁棒性。仿真和实验结果表明,所提算法在参数辨识的快速性、准确性、稳定性方面均表现优越,辨识结果能够满足对永磁同步电机进行建模和仿真的精度要求。

In order to solve the problem that the parameter identification of permanent magnet synchronous motor(PMSM)using swarm intelligence algorithm is easy to fall into local optimization in the late stage,resulting in poor identification accuracy,a modified bacterial foraging algorithm(MBFA)integrating niche technology was proposed.By constructing the target tracking function,this method can quickly and accurately identify the d-axis inductance,q-axis inductance,stator resistance and permanent magnet flux linkage of the motor by using the directly measurable signals of motor voltage,current and speed.In the process of identification,Quasi Monte Carlo sampling of the target solution space by introducing lattice criteria improves the global search ability of the algorithm.The online multi group collaborative search strategy based on niche technology improves the search efficiency and optimization accuracy of the algorithm.Finally,a real-time population monitoring and dynamic updating mechanism was introduced to ensure the robustness of the algorithm in the whole optimization process.Simulation and experimental results show that the proposed algorithm is superior in the speed,accuracy and stability of parameter identification.The identification results can meet accuracy requirements of modeling and simulation of permanent magnet synchronous motor.