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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]

A basic, fast, and accurate steady-state finite-element analysis (FEA) based parameter and performance calculation method for the wound-rotor induction motor is presented. The method couples the circuit equations with field equations using the principle of frozen permeabilities to present the voltage-fed induction motor. The effects of saturation saliency and cross-magnetization are accurately taken into account. The method is verified by direct comparison with the current- and torque-slip frequency curves obtained from a commercial FEA package. [ABSTRACT FROM PUBLISHER]

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]

This work deals with the differential-evolution (DE)-based method for simultaneous identification of the electric, magnetic, and mechanical subsystem parameters of a line-start interior permanent magnet synchronous motor (LSIPMSM). The parameters are determined in the optimization procedure using the dynamic model of the LSIPMSM; the time behavior of voltages, currents, and speed measured on the tested LSIPMSM; and the DE, which is applied as the optimization tool. During the optimization procedure, the DE changes the parameters of the LSIPMSM dynamic model in such a way that the differences between the measured and calculated time behaviors of individual state variables are minimized. This paper focuses on the objective function definition, the constraint settings for individual parameters, the normalization of parameters, and, above all, the test and measurement procedures performed on the LSIPMSM, which all together make it possible to determine the LSIPMSM dynamic model parameters valid for a broad range of operation, thus ensuring proper evaluation of the LSIPMSM's line-starting capability. Some of the LSIPMSM parameters that can be determined by finite-element analysis and experimental methods are compared to the values obtained by the DE, thus validating the DE-based approach. [ABSTRACT FROM PUBLISHER]