用于系统能效动态评估的异步电机参数辨识方法

发布时间：2018-10-04 18:56

【摘要】：对电机系统进行能效动态评估的一个重要方法是等效电路法,即利用异步电机等效电路参数求解电机内部各项损耗,进而估算得出电机效率。但电机参数在不同工况下变化明显,若不对参数进行在线辨识,系统能效评估往往达不到预期的效果,进而不能及时有效地采用节能措施,会造成很大的电能浪费。因此,为了能够利用该方法对电机能效进行准确评估,首先需要解决的问题就是对电机等效电路参数进行在线辨识,进而利用辨识得到的参数,完成电机能效的在线动态评估。本文通过理论分析、仿真计算以及实验研究相结合的方法,对异步电机参数的在线辨识进行了系统研究,主要工作如下： 1.针对异步电机传统的空载—短路试验法获取等效电路参数的弊端,提出带遗忘因子递推最小二乘法的参数辨识方法。首先,利用基于dq0坐标系的异步电机动态方程,推导出标准最小二乘法形式的电机参数辨识模型；在充分考虑影响辨识结果因素的基础上,设计了一种带遗忘因子递推最小二乘法的参数辨识算法；最后,以37kW、11kW和5.5kW三台电机为仿真实例,并对其中的5.5kW电机进行参数辨识的试验研究。仿真和试验结果表明,文中提出的辨识方法能有效的在线辨识电机的等效电路参数。 2.考虑到上述方法中,转子电阻的辨识精度受转速影响较大,研究了可计及转速波动的基于RLS与MRAS的鼠笼式异步电机转子电阻在线辨识方法。以一台5.5kW异步电机为例进行了仿真和实验研究,并将参数辨识方法应用于11000kW电机的现场实例分析。结果表明,文中方法适用于转速波动明显且转子电阻变化较大的电机复杂运行工况,且具有计算量少、准确度高以及较好的动态跟踪辨识性能。 3.文中将上述电机参数在线辨识方法用于实际的电机能效动态评估。以一台5.5kW电机为例,利用参数辨识方法获取电机参数,运用等效电路法评估出电机的能效水平；并与传统的扭矩仪试验方法的能效评估结果对比,进一步验证了文中提出的异步电机参数在线辨识方法的正确性与可行性。
[Abstract]:An important method for dynamic evaluation of motor energy efficiency is the equivalent circuit method, that is, the equivalent circuit parameters are used to solve the internal losses of the motor, and then the efficiency of the motor is estimated. However, the motor parameters change obviously under different working conditions. If the parameters are not identified on line, the system energy efficiency evaluation often can not achieve the desired results, and then can not use energy saving measures in time and effectively, which will cause a lot of energy waste. Therefore, in order to accurately evaluate the motor energy efficiency using this method, the first problem to be solved is to identify the equivalent circuit parameters online, and then to use the identified parameters to complete the on-line dynamic evaluation of motor energy efficiency. In this paper, the on-line identification of asynchronous motor parameters is systematically studied through theoretical analysis, simulation and experimental research. The main work is as follows: 1. Aiming at the drawback of the traditional no-load short-circuit test method to obtain the equivalent circuit parameters of asynchronous motor, a recursive least square method with forgetting factor is proposed for parameter identification. First of all, using the dynamic equation of asynchronous motor based on dq0 coordinate system, the identification model of motor parameters in the form of standard least square method is derived. A parameter identification algorithm based on recursive least square method with forgetting factor is designed. Finally, three electrical machines, 37kW 11kW and 5.5kW, are taken as simulation examples, and the experimental research on parameter identification of 5.5kW motor is carried out. Simulation and experimental results show that the proposed identification method can effectively identify the equivalent circuit parameters of the motor. 2. Considering that the accuracy of rotor resistance identification is greatly affected by rotational speed, an on-line rotor resistance identification method based on RLS and MRAS for squirrel cage induction motor is studied. The simulation and experiment of a 5.5kW asynchronous motor are carried out, and the method of parameter identification is applied to the field analysis of 11000kW motor. The results show that the proposed method is suitable for complex operating conditions of motor with obvious fluctuation of rotational speed and large variation of rotor resistance, and has the advantages of less calculation, higher accuracy and better dynamic tracking and identification performance. 3. In this paper, the on-line identification method of motor parameters is applied to the dynamic evaluation of motor energy efficiency. Taking a 5.5kW motor as an example, the parameter identification method is used to obtain the motor parameters, and the equivalent circuit method is used to evaluate the motor energy efficiency level, and the results are compared with those of the traditional torque-meter test method. The correctness and feasibility of the proposed on-line identification method for asynchronous motor parameters are further verified.
【学位授予单位】：华北电力大学
【学位级别】：硕士
【学位授予年份】：2014
【分类号】：TM343

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