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In the existing offline inductance identification methods of permanent magnet synchronous machines (PMSMs), dq-axis voltage equations are generally adopted and the inverter nonlinearity compensation is necessary, which complicates the identification process and restricts the generality. In order to solve the problem, an offline inverter-nonlinearity-immune inductance identification method is proposed in this article based on the PMSM equivalent impedance model. The method can identify the dq-axis inductances by injecting both dc and high frequency signals, where the magnetic saturation and the cross saturation are considered. Besides, an inductance surface forming algorithm is introduced based on the bilinear interpolation. The continuous dq-axis inductance surfaces can be then obtained by only a few identified data, so that the time of data sampling and processing is greatly shorten compared with the existing methods. To enhance the accuracy of the identification method, the digital delay error and the sampling error in zero current zone are investigated and compensated. The effectiveness of the proposed method is validated on three different test machines. [ABSTRACT FROM AUTHOR]

The extensive application of vector phase shift modulation method in permanent magnet synchronous motor drive systems with single dc-link current sensor results in obvious current ripple in the low-speed region. The patterns of these ripples vary greatly in different sectors, leading to nonnegligible phase current reconstruction errors. In this article, an estimation of the local average current change rate (ELAC2R) based reconstruction error suppression method within the switching cycle is proposed to resolve this problem. By comparing the actual and the ideal sampling points, the increase of current ripple and the reconstruction error caused by the combination of the vector phase shift and the single current sensor sampling in the low-speed region are modelled and analyzed. Then according to equivalent modelling of the current ripple between the ideal and the actual sampling points, the more accurate phase current information can be quickly obtained based on the sampling value. The compensation process is significantly simplified and the computation burden of the processor is reduced. Experimental results show that the performance of current reconstruction can be improved effectively with less harmonic content with the proposed method. [ABSTRACT FROM AUTHOR]

In the voltage source inverter applications, inverter nonlinearities would affect the parameter identification process in many ways. Hence, this article proposes an offline identification method for resistance and dq-axis inductance surface by considering the inverter nonlinearity characteristics. A variable amplitude square-wave injection (VASI) scheme is proposed for the dq-axis inductance identification. The VASI method achieves the inductance identification with a novel data sampling strategy. Meanwhile, it can also establish the inductance surfaces by only a few identified data points with a polynomial fitting algorithm, which greatly reduces the identification time compared with the existing methods. The resistance identification is realized by a slope signal injection method, in which the effect of IGBT voltage drop is analyzed. In order to improve the identification accuracy, the inverter nonlinearities are compensated by a self-learning method considering the zero-axis voltage at different rotor positions. At the same time, the sampling error in zero current zones of abc-phases is researched. In order to verify the effectiveness and generality, the proposed method is carried out on two different test machines and confirmed by finite element analysis. [ABSTRACT FROM AUTHOR]