基于MEA-BP神经网络的开关磁阻电机静态电磁特性建模  

Modeling of Static Electromagnetic Characteristics of Switched Reluctance Motor Based on MEA-BP Neural Network

作　　者：王成敏 王爱元[1,2] 姚晓东 殷世雄 李吉程 WANG Chengmin;WANG Aiyuan;YAO Xiaodong;YIN Shixiong;LI Jicheng(School of Electrical Engineering,Shanghai Dianji University,Shanghai 201306,China;Minge New Type Motor Electronic Control Research Institute,Gaoming District,Foshan City,Foshan 528500,China)

机构地区：[1]上海电机学院电气学院,上海201306 [2]佛山市高明区明戈新型电机电控研究院,广东佛山528500

出　　处：《电机与控制应用》2022年第5期64-68,共5页Electric machines & control application

摘　　要：建立精确的开关磁阻电机(SRM)模型对于改善SRM的性能和控制效果有着重要的影响。针对SRM运行时磁路的高度饱和和严重非线性问题,提出了基于思维进化算法(MEA)优化的反向传播(BP)神经网络算法的SRM非线性模型。利用ANSYS Maxwell软件建立了四相8/6极SRM模型并进行有限元计算,通过仿真和试验值的对比验证了该模型的精度比未经MEA优化的BP神经网络模型更高,可以更好地反映SRM运行时的磁链特性和转矩特性,且具有较好的泛化能力。Establishing an accurate switched reluctance motor(SRM)model has an important impact on improving the performance and control effect of SRM.For the problems of high saturation and serious nonlinearity of magnetic circuit during SRM operation,an SRM nonlinear model based on back propagation(BP)neural network algorithm optimized by mind evolutionary algorithm(MEA)is proposed.A four-phase 8/6-pole SRM model is established by using ANSYS Maxwell software and the finite element calculation is carried out.It is verified through the comparison between the simulated value and the experimental value that the model has higher accuracy than the BP neural network model without MEA optimization.It can better reflect the flux linkage and torque characteristics during SRM operation,and has better generalization ability.

关 键 词：开关磁阻电机 非线性建模 BP神经网络 思维进化算法 

分 类 号：TM352[电气工程—电机]