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A novel online multiparameter identification method is proposed for interior permanent magnet (IPM) machines considering cross-coupling magnetic saturation. The identification method is based on the combination of small-signal impedance model and fundamental frequency signal model under dq-axes. To analyze the nonlinearity of IPM machines, cross-coupling magnetic saturation effect is introduced into online multiparameter identification for the first time. Based on this model, a phase compensation strategy and orthogonal decomposition in phasor domain is proposed to decouple the complicated mathematical model into four linear equations. By injecting voltage small-signals into dq-axes, multiparameter of IPM machine including adjusted apparent inductance, incremental self-inductance, incremental mutual-inductance can be identified online in a wide operation region. During this process, no additional computing equipment is required. The proposed method is carried out on a tested IPM machine and verified by the finite-element analysis. [ABSTRACT FROM AUTHOR]

Existing online motor parameter identification methods mostly depend on the fundamental frequency voltage equations, which lead to the unsatisfactory identification effect at low current and low speed operation. This article proposes a parameter identification method based on the high frequency (HF) equivalent impedance model of permanent magnet synchronous motor with HF signal injection at both the dq-axes. This method identifies the resistance and the dq-axis inductances offline and online, along with the flux linkage online. In order to improve the identification accuracy, the parameter sensitivity analysis-based algorithm is proposed to detect the resistance and the flux linkage. Meanwhile, the inverter nonlinearities and the HF influence on parameter identification are compensated effectively. In order not to affect the normal operation of the motor drive, the selection of the amplitude, and the frequency of the injected signal is investigated. The proposed method is validated on a 2.2-kW motor and confirmed by finite element analysis. The experimental results show the good identification effect in different operation conditions. [ABSTRACT FROM AUTHOR]

This paper proposes predictive-control-based direct power control (DPC) with an adaptive online parameter identification technique for ac-dc active front ends (AFEs) to overcome model mismatch and parameter uncertainty. Based on least-squares estimation, the input inductance and input resistance of the AFE are calculated every sampling period using sampled input currents and input voltages. Because the online-tuned input inductance and resistance are updated for the controller, the proposed predictive-control-based DPC with the adaptive parameter identification technique can mitigate performance degradation resulting from the model uncertainty of the model predictive controller without any additional sensors. Therefore, the AFE generates sinusoidal input currents with a unity power factor despite parameter uncertainty. [ABSTRACT FROM AUTHOR]