On-line Temperature Estimation of Permanent Magnet Motor Based on Lumped Parameter Thermal Network Method
Shi Wei1, Luo Kaichuan1, Zhang Zhouyun2
1. College of Urban Rail Transit, Shanghai University of Engineering Science, Shanghai 201620, China; 2. Shanghai Electric Drive Co., Ltd Shanghai 201806 China
Abstract：The on-line temperature estimation of permanent magnet motor (PMM) can prevent the motor damage caused by over temperature, especially reduce the risk of irreversible demagnetization of permanent magnet and improve the safety of PMM. Traditional on-line temperature estimation of lumped parameter thermal network (LPTN), the influence of LPTN topology with different scales on the speed and accuracy of on-line estimation has not been deeply studied. At the same time, the nonlinearity of thermal resistance unit under variable working conditions, and the error accumulation caused by loss calculation generate the deviation between the estimated temperature and the actual value. In order to solve these problems, this paper compares the low order gray box LPTN models of PMM with different nodes, and proposes the weighted multi-innovation strength tracking extended Kalman filter (WMI-STEKF) algorithm to carry out on-line parameter identification and temperature estimation with high accuracy under variable working conditions. Firstly, compared the LPTN models, the LPTN gray box models of PMM with different nodes are established. Next, the experimental temperature platform of variable condition temperature of PMM is built. Based on the experimental temperature data, the models are compared and analyzed. The optimal LPTN model for on-line temperature estimation of PMM is obtained. Third, the time-varying fading factor is introduced into the extended Kalman filtering algorithm to adjust the real-time gain matrix, so as to improve the robustness of the model and the accuracy of the extended Kalman filtering algorithm identification. At the same time, the residual scalar in extended Kalman filtering algorithm is extended to innovation matrix to improve the adaptability of the system to nonlinear systems. WMI-STEKF algorithm is used in on-line parameter identification and temperature estimation. According to the actual experimental temperature data, the grey box thermal network models with four different node models are evaluated. The five nodes model has the best accuracy, convergence speed and stability. At the same time, the average error and maximum error of the temperature estimation results of the five nodes model which are 3.53 ℃ and 5.87 ℃ respectively are lower than other nodes number models. In the online parameter identification and temperature estimation of LPTN model using the WMI-STEKF algorithm, six consecutive working conditions of 45kW, 3500r/min; 45kW,3750r/min; 45kW,4000r/min; 45kW,4500r/min; 16kW,1000r/min; 32kW, 2000r/min are implemented. After temperature identification, the error between the estimated permanent magnet temperature and the measured permanent magnet temperature is within 3 ℃. The following conclusions are drawn: 1) The low order gray five node model LPTN model can meet the needs of online temperature estimation. Through system identification and experimental data, the model comparison under different working conditions and quantitative temperature estimation index evaluation, it is the optimal online estimation model for experimental motor. 2) Based on multi-innovation theory and strong tracking extended Kalman filter algorithm, the accumulated error in the temperature estimation process which caused by model simplification and thermal resistance change is effectively compensated under variable working conditions. This algorithm improves the applicability in the strongly nonlinear system of motor thermal model, and robustness of process parameter changes in parameter identification, and has high accuracy in online temperature identification.
师蔚, 骆凯传, 张舟云. 基于热网络法的永磁电机温度在线估计[J]. 电工技术学报, 0, (): 2-2.
Shi Wei, Luo Kaichuan, Zhang Zhouyun. On-line Temperature Estimation of Permanent Magnet Motor Based on Lumped Parameter Thermal Network Method. Transactions of China Electrotechnical Society, 0, (): 2-2.
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