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 online temperature estimation of permanent magnet motors (PMM) can prevent motor damage caused by over temperature, especially reduce the risk of irreversible demagnetization of permanent magnet and improve the safety of PMM. For traditional online 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 yet to be deeply studied. At the same time, the thermal resistance unit nonlinearity and the error accumulation from loss calculation generate the deviation between the estimated temperature and the actual value. Therefore, after comparing the low-order gray box LPTN models of PMM with different nodes, this paper 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, 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 online 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 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 the extended Kalman filtering algorithm is extended to the 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. The grey box thermal network models with four different node models are evaluated according to the experimental temperature data. The five-node model has the best accuracy, convergence speed, and stability. The average error and maximum error of the temperature estimation results are 3.53 ℃ and 5.87 ℃, respectively, lower than other node number models. In the online parameter identification and temperature estimation of the LPTN model using the WMI-STEKF algorithm, six consecutive working conditions of 45 kW, 3 500 r/min; 45 kW, 3 750 r/min; 45 kW, 4 000 r/min; 45 kW, 4 500 r/min; 16 kW, 1 000 r/min; 32 kW, 2 000 r/min are implemented. After temperature identification, the error between the estimated and measured permanent magnet temperatures is within 3 ℃. The following conclusions are drawn: (1) The low-order gray five-node LPTN model can meet the needs of online temperature estimation. Through system identification and experimental data, the proposed model is the optimal online estimation model for experimental motors under different working conditions and quantitative temperature estimation index evaluation. (2) Based on multi-innovation theory and strong tracking extended Kalman filter algorithm, the accumulated error in the temperature estimation process caused by model simplification and thermal resistance change is effectively compensated under different working conditions. This algorithm improves the applicability in the strongly nonlinear system of motor thermal model and the robustness of process parameter changes in parameter identification. It 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, 2023, 38(10): 2686-2697.
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, 2023, 38(10): 2686-2697.
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2023, Vol. 38 
