永磁同步电机参数辨识优化算法研究

发布时间：2018-06-21 05:08

  本文选题：永磁同步电机 + 参数辨识　； 参考：《江南大学》2016年硕士论文

【摘要】：由于永磁同步电机(Permanent Magnet Synchronous Motor,PMSM)具有结构简单,体积小,重量轻,损耗小,效率高等特点;并且国内的稀土永磁材料储存丰富,所以能够得到快速的推广和应用于高性能驱动系统及其他工业领域。在实际的运行过程中,永磁同步电机系统是强耦合,非线性,时变的动态系统,并且PMSM系统参数容易受到温度、磁通饱和、定子电流等因素的影响,这些影响不仅降低了运行的可靠性也提高了控制系统的难度。而高性能的PMSM控制系统的实现依赖于精确的电机参数。所以,对电机的参数进行准确且实时的辨识是提高PMSM控制系统的前提。本文首先介绍了永磁同步电机的基本结构以及目前的参数辨识技术,由基本的永磁同步电机的三相静止坐标下的数学模型,根据矢量变换的原理,将其变换到两相旋转d-q轴坐标系下的数学模型。介绍了基本的矢量控制原理,并综合比较几种常见的解耦控制方法,说明了使用0di?的控制方法的原因,同时介绍了空间矢量脉宽调制技术的原理及实现。针对传统的粒子群算法以及最小二乘法在处理电机多参数离线辨识问题时具有速度慢,误差高的问题,提出了将珊瑚礁算法应用于PMSM的多参数离线辨识中。将辨识的结果与使用其他传统方法辨识的PMSM参数从辨识的速度以及精度两个方面进行对比,验证了珊瑚礁算法的有效性。同时针对基本珊瑚礁算法在辨识电机离线参数时可能会陷入局部最优的问题,提出了将高斯与柯西变异引入珊瑚礁算法的运行过程中,将改进珊瑚礁算法与基本珊瑚礁算法同时应用于PMSM的多参数离线辨识中,对比得出改进算法在收敛速度以及精确度方面的优势。针对遗忘因子递推最小二乘辨识对电机参数进行在线辨识时受到遗忘因子大小影响、辨识结果不稳定的问题,提出了将多新息算法与遗忘因子递推最小二乘算法相结合解决,由于新息长度的选择要同时考虑算法的收敛速度以及算法辨识的精确度,所以通过实验选取合适的新息长度。通过参数恒定与参数阶跃变换两种情况下的仿真实验可以得出多新息遗忘因子递推最小二乘法在对PMSM参数在线辨识时具有更好的稳定性、收敛性。尤其是参数变化时的跟踪性能良好。
[Abstract]:Permanent Magnet synchronous Motor (PMSM) has the advantages of simple structure, small size, light weight, low loss and high efficiency. Therefore, it can be quickly popularized and applied to high performance drive systems and other industrial fields. The PMSM system is a strongly coupled, nonlinear and time-varying dynamic system in the actual operation process, and the PMSM system parameters are easily affected by temperature, flux saturation, stator current and other factors. These effects not only reduce the reliability of operation, but also improve the difficulty of control system. The realization of high performance PMSM control system depends on precise motor parameters. Therefore, accurate and real-time identification of motor parameters is the prerequisite to improve PMSM control system. This paper first introduces the basic structure of permanent magnet synchronous motor and the current parameter identification technology. According to the principle of vector transformation, the basic mathematical model of permanent magnet synchronous motor under three-phase static coordinates is introduced. It is transformed to the mathematical model of two phase rotating d-q axis coordinate system. The basic principle of vector control is introduced, and several common decoupling control methods are compared synthetically. At the same time, the principle and realization of space vector pulse width modulation are introduced. Aiming at the slow speed and high error of traditional particle swarm optimization (PSO) and least square method (LSM) in the process of multi-parameter off-line identification of motor, a new method of coral reef algorithm is proposed for multi-parameter off-line identification of PMSM. The validity of the coral reef algorithm is verified by comparing the identification results with the PMSM parameters identified by other traditional methods in terms of the speed and accuracy of the identification. At the same time, aiming at the problem that the basic coral reef algorithm may fall into local optimum when identifying the off-line parameters of the motor, it is proposed to introduce Gao Si and Cauchy mutation into the running process of the coral reef algorithm. The improved coral reef algorithm and the basic coral reef algorithm are applied to the multi-parameter off-line identification of PMSM at the same time. The advantages of the improved algorithm in convergence speed and accuracy are compared. In order to solve the problem that the forgetting factor is affected by the magnitude of the forgetting factor and the result of the identification is unstable when the forgetting factor recursive least square identification is carried out on line, a new algorithm is proposed to solve the problem, which combines the multiple innovation algorithm with the forgetting factor recursive least square algorithm. Because the convergence speed of the algorithm and the accuracy of algorithm identification must be taken into account in the selection of innovation length, the appropriate innovation length is selected through experiments. Through the simulation experiments under the condition of constant parameters and step transformation of parameters, it can be concluded that the recursive least square method of multi-innovation forgetting factor has better stability and convergence in on-line identification of PMSM parameters. Especially, the tracking performance is good when the parameters change.
【学位授予单位】：江南大学
【学位级别】：硕士
【学位授予年份】：2016
【分类号】：TM341
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本文编号：2047367