Search

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]

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]

Factors such as machining precision and electrical failures may lead to phase inductance unbalance in a switched reluctance motor (SRM). Under unbalanced inductance conditions, most of the traditional sensorless schemes would be invalid if no modification is applied. To prove the existence of this problem, an inductance partition based rotor position estimation scheme under balanced and unbalanced inductance conditions are investigated and compared for identifying the position estimation error characteristics. The theoretical analysis shows that the position estimation error is highly dependent on the degree of the inductance unbalance. To maintain stable sensorless operation under unbalanced condition, a modified full-cycle inductance partition based rotor position estimation scheme with unbalanced inductance adaptive capability is proposed in this paper. In this method, the linear region of each phase full-cycle inductance is relocated with the predetermined inductance thresholds. By locating the linear inductance regions, the rotor position can be estimated from a simple partition linear model between the rotor position and the phase inductance. This method is easy for implementation without additional hardware and complex calculation. Experiments are performed on a 12/8 structure SRM and the results verify the effectiveness of the proposed method. [ABSTRACT FROM PUBLISHER]