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A global parameter estimation method for a permanent magnet synchronous machines (PMSM) drive system is proposed, where the electrical parameters, mechanical parameters, and voltage-source-inverter (VSI) nonlinearity are regarded as a whole and parameter estimation is formulated as a single parameter optimization model. A dynamic learning estimator is proposed for tracking the electrical parameters, mechanical parameters, and VSI of PMSM drive by using dynamic self-learning particle swarm optimization (DSLPSO). In DSLPSO, a novel movement modification equation with dynamic exemplar learning strategy is designed to ensure its diversity and achieve a reasonable tradeoff between the exploitation and exploration during the search process. Moreover, a nonlinear multiscale based interactive learning operator is introduced for accelerating the convergence speed of the $Pbest$ particles; meanwhile a dynamic opposition-based learning strategy is designed to facilitate the $gBest$ particle to explore a potentially better region. The proposed algorithm is applied to parameter estimation for a PMSM drive system. The results show that the proposed method has better performance in tracking the variation of electrical parameters, and estimating the immeasurable mechanical parameters and the VSI disturbance voltage simultaneously. [ABSTRACT FROM AUTHOR]

This paper describes a self-sensing control scheme based on pulse-voltage-injection for permanent-magnet synchronous machines (PMSMs). Multiple saliencies, as well as the saturation and reluctance-based coupling effects, are studied by means of analytical methods, and are proved with finite element method simulations and measurements. These phenomena cause a constant estimated error of the rotor angle, as well as periodic errors with harmonics of order 6 k. The primary saliency of the considered interior PMSM with concentrated windings is strongly dependent on the load, so that the self-sensing capability disappears at some critical operating points. In this study, the parameters of the multiple saliencies are identified based on the proposed self-sensing method. The secondary saliencies are compensated with a closed loop control structure. The effectiveness of the proposed method is confirmed by experimental results. [ABSTRACT FROM PUBLISHER]

A widely used method for the magnetic field calculation in permanent magnet (PM) machines is the harmonic modeling (HM) method. Despite its many advantages, the application of this method is limited to machines in which armature currents, as a source of the magnetic field, are known. Since most of the PM machines are supplied with three-phase voltages, any variation of parameters in the armature electric circuit could lead to the uncertainty in knowing the armature currents, and therefore, limit use of the HM method. In this paper, an approach for overcoming this limitation is presented, which enables the calculation of the magnetic field in the PM machines without a priori knowledge of the armature currents. [ABSTRACT FROM AUTHOR]