DC-Signal-Injection-Based Online Parameters Identification for Permanent Magnet Synchronous Machine Considering Variation of Equivalent Electromagnetic Loss Resistance
Ma Yilin1,2, Yuan Hao1,2, Yin Wei1,2, Yang Huan1,2
1. College of Electrical Engineering Zhejiang University Hangzhou 310027 China; 2. Zhejiang Provincial Key Laboratory of Electrical Machine Systems Hangzhou 310027 China
Abstract Permanent magnet synchronous machines (PMSMs) are widely used for their high operating efficiency. During the application, the parameters of PMSMs may vary a lot when considering the effect of temperature, magnetic saturation, and so on. In this case, the performance of some optimal control strategies, such as the maximum efficiency control and current decoupling control, may decay. Thus, to achieve high-performance control, online parameter identification is important. However, there are few parameter identification methods considering the ferromagnetic loss and its variation with currents, while the ferromagnetic loss occupies a great proportion of the PMSMs' loss. Therefore, this paper integrates ferromagnetic loss and copper loss as electromagnetic loss. Then, an improved DC-signal-injection-based online parameters identification is proposed considering equivalent electromagnetic loss resistance variation. Firstly, the magnetic saturation of PMSMs and the variation of electromagnetic loss with current are discussed. Due to the magnetic saturation characteristic of ferromagnetic materials, the magnetic permanence of PMSMs may be decreased as the current rises, which, in turn, affects the inductance and flux linkage. Besides, when considering the change of current vectors, the copper loss may be variable due to the proximate effect. Meanwhile, the ferromagnetic loss may also vary with the changeable magnetic distribution. An improved series electromagnetic loss resistance model is proposed. Apparent flux linkage and incremental inductance are used to characterize the magnetic saturation, and change rates of the equivalent resistance are used to represent variations in electromagnetic loss. Secondly, 4 groups of d-/q-axis operating currents are chosen based on the criterion of alternating changes in d-/q-axis currents. According to the analysis, if the current increment of each change is non-zero, the steady-state voltage equations of these 4 groups are linearly independent. That is, the corresponding coefficient matrix is full-rank, allowing for the full parameters identification. Thirdly, a least mean square (LMS) algorithm is adopted to solve the 4 groups of steady-state voltage equations due to the complexity of analytical computation arising from the high-rank coefficient matrix. The LMS algorithm calculates the gradient of voltage estimation errors' mean square to each model parameter based on the measured voltages, currents, and speeds. Then, the model parameters are updated iteratively based on the corresponding gradients at each step. After iteration, the model parameters that meet the preset voltage estimation accuracy are obtained. Finally, the accuracy of the proposed method is validated using a prototype PMSM. Experimental results show that, compared with the sinusoidal-signal-injection-based method and the traditional DC-signal-injection- based method, the proposed method is more precise in identifying electromagnetic resistance and apparent flux linkage, with an average relative error of less than 2.3%. In addition, the proposed method has high identification accuracy for torque and incremental inductance, with average relative errors less than 0.9% and 3.5%, respectively. However, there is room for improving the identification performance of the equivalent resistance change rate.
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2023, Vol. 38 
