摘要
针对同步磁阻电机运行过程中严重的转矩脉动问题,提出一种基于机器学习的同步磁阻电机转子结构多目标智能优化方法。首先,通过磁路分析获得同步磁阻电机待优化转子结构参数,并利用有限元法进行敏感度分析,确定待优化变量与范围。其次,引入深度神经网络建立同步磁阻电机非参数快速计算模型,构建待优化变量与转矩之间的非线性映射关系,完成电机电磁特性精确建模。在此基础上,提出一种基于强化学习的改进粒子群算法,根据强化学习中奖励函数机制在线调整优化算法的学习因子,提高粒子群算法的收敛速度和全局寻优精度。最后,以最小化转矩脉动和提高平均转矩为优化目标,采用改进粒子群算法与深度神经网络模型,实现同步磁阻电机转子结构参数的多工况全局优化。仿真与实验结果表明,所提出方法优化后的同步磁阻电机相较初始电机模型,不仅具有更低的转矩脉动,而且平均转矩输出略有增加。
To address the issue of serious torque ripple in synchronous reluctance motors,a multi-objective intelligent optimization method for the rotor structure of synchronous reluctance motors is proposed based on machine learning.First,the rotor structural parameters to be optimized for the synchronous reluctance motor are obtained through magnetic circuit analysis,and sensitivity analysis is conducted using the finite element method to determine the variables and their ranges for optimization.Second,a deep neural network is introduced to establish a non-parametric rapid calculation model for the synchronous reluctance motor,and a nonlinear mapping relationship between the optimized variables and torque is constructed to accurately model the electromagnetic characteristics of the motor.Based on this,an improved particle swarm optimization algorithm based on reinforcement learning is proposed.This approach adjusts the learning factors of the optimization algorithm online according to the reward function mechanism in reinforcement learning,improving the convergence speed and global optimization accuracy of the particle swarm optimization algorithm.Finally,with the objectives of minimizing torque ripple and increasing average torque,the improved particle swarm optimization algorithm and the deep neural network model are used for global optimization of the motor rotor structural parameters under multiple operating conditions.The simulation and experimental results show that the optimized synchronous reluctance motor using the proposed method not only has lower torque ripple compared to the initial motor model,but also slightly increases the average torque.
作者
王子贺
李存贺
焦提操
鲁炳林
熊立新
Wang Zihe;Li Cunhe;Jiao Ticao;Lu Binglin;Xiong Lixin(School of Electrical and Electronic Engineering,Shandong University of Technology,Zibo 255049,China;Shandong Kehui Power Automation Co,Ltd.Zibo 255087,China)
出处
《电子测量与仪器学报》
CSCD
北大核心
2024年第9期116-126,共11页
Journal of Electronic Measurement and Instrumentation
基金
国家自然科学基金(62073200)
山东省泰山学者项目(TSQN202306191)资助。
关键词
同步磁阻电机
转矩脉动
深度神经网络
粒子群算法
强化学习
synchronous reluctance motors
torque ripple
deep neural network
particle swarm optimization
reinforcement learning
