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Torque ripple reduction has become an active research area for permanent magnet synchronous machine (PMSM) drives. This paper presents a novel torque ripple reduction method based on harmonic current control. In the proposed method, the optimal harmonic current solution based on the torque ripple model with minimum stator resistive loss is derived in the rotor reference frame (RRF). The optimal harmonic currents are injected by the proposed harmonic current controller (HCC), wherein the magnitude of harmonic currents are defined as controller variables. To estimate the harmonic currents, the proposed method also utilizes the least mean square (LMS) based adaptive filter (AF), wherein the coefficients are defined as feedback to the proposed HCC. The proposed methodology is demonstrated using both simulations and experiments and is verified to reduce torque ripples of PMSM drives over a wide range of speeds. [ABSTRACT FROM AUTHOR]

Deadbeat predictive current control (DPCC) can predict motor behavior based on SPMSM model. However, during the operation of motor system, motor parameters (such as stator inductance and flux linkage) vary frequently according to different working conditions, which may lead to controller parameter mismatch, causing current harmonic content to increase and efficiency to decrease. In order to solve these problems caused by parameter variation, first, this paper proposes a current prediction error model by considering uncertainties of model parameters. Second, stator inductance and flux linkage are decoupled based on current prediction error model, which can reduce the interaction between parameters. Finally, the Kalman Filter (KF) algorithm is presented to filter the decoupled parameters. It is shown that the stator inductance and flux linkage can be identified accurately and the complexity of computation can be simplified. The traditional DPCC method, Extended Kalman Filter (EKF) based DPCC method and the proposed DPCC method are comparatively analyzed in this paper. Simulation and experiment indicate that the proposed parameter decoupling identification method can effectively reduce current harmonic content, current fluctuation and current tracking errors caused by parameter mismatch. [ABSTRACT FROM AUTHOR]

This article discusses a method to consider the effects of the manufacturing uncertainties on the additional cogging torque harmonics. With this method, the sensitivity analysis can be incorporated into the design stage instead of just checking it afterwards. With the authors’ previous work of the worst-uncertain-combination-analysis (WUCA) method, several design parameters and their effects on the additional cogging torque harmonics under uncertainties are discussed. It is demonstrated that a design with inherent robustness can be obtained through the proper selection of pole-arc coefficient and slot-opening width. In addition, a sensitivity index is proposed to estimate the effects of parameter uncertainties on torque harmonics. Some suggestions on improving the design robustness are consequently provided from the aspects of tolerances control and parameter design. The deductions are verified through finite element analysis (FEA) on different pole/slot configurations. [ABSTRACT FROM AUTHOR]

One of the main obstacles to applying the robust optimization for the permanent magnet motors is the high computational burden, which is mainly caused by the robustness evaluation considering the manufacturing uncertainties. In this article, efforts are made to speed up the process of robust optimization, from the aspects of reducing the computational cost, and avoiding the computing resources being wasted. Firstly, several widely used methods, aiming to identify the worst-case performance under uncertainties, are examined, and compared. And the efficient worst-uncertain-combination-analysis (WUCA) method is consequently adopted to significantly reduce the computational cost of robustness evaluation. From another aspect, the parameter rounding (PR) operation is inevitable due to the constraint of manufacturing accuracy, but its adverse effects on the optimization are often ignored. It is found that parts of the computing resources might be wasted during the optimization. A new PR during the calculation (PRDC) method is proposed, and incorporated into the process of robust optimization. Compared with the conventional one, similar results can be achieved with the computational time reduced by approximately $\text{11}\%$ in the PRDC modified robust optimization. [ABSTRACT FROM AUTHOR]

Permanent-magnet (PM) motors have been widely used in industrial applications and automobiles. However, owing to the design tolerances, manufacturing uncertainties, and material inconsistency, deviations from the ideal motor inevitably occur with parasitic effects, such as additional cogging torque and vibration. One of the most significant obstacles for evaluating these uncertainties is the large computational burden caused by the countless uncertain combinations, which must be computed via the finite-element method (FEM). Herein, a worst-uncertain-combination-analyze (WUCA) method is proposed to significantly reduce the computational cost. A widely used analytical method is modified to identify the origin of additional cogging-torque harmonics caused by uncertainties. Different types of uncertainties for the surface-mounted PM machine and interior PM machine can be analyzed simultaneously. With the WUCA method, the worst-case combinations can be confirmed theoretically; thus, FEM calculations for only a few combinations of uncertainties are required, rather than thousands. Compared with the widely adopted design of experiments based uncertain combining methods, the worst-case cogging torque obtained from the WUCA is higher. FEM verification of different pole/slot configurations revealed that the WUCA method was a general and effective method for quickly estimating the worst-case cogging torque under manufacturing uncertainties. [ABSTRACT FROM AUTHOR]

Model predictive current control (MPCC) can predict future motor behavior according to a motor model. In practice, however, motor parameters will vary at run time, and the parameter mismatch disturbances caused by the variation in motor parameters will deteriorate the MPCC performance. To suppress the parameter mismatch disturbances effectively, this paper proposes a modified MPCC with a current variation update mechanism. In contrast with the traditional current prediction equation that contains crude model parameters, the modified current prediction equation contains only measured information, taking advantage of the proposed current variation update mechanism, which can update the modified prediction equation within each sampling period. A simulation established by MATLAB software indicates that the proposed method can effectively suppress the parameter mismatch disturbances. Experiments are carried out to verify the correctness of the proposed method. [ABSTRACT FROM AUTHOR]

In an open-end winding permanent magnet synchronous machines drive with single dc voltage source, zero-sequence current (ZSC) can lead to a high current stress of power modules and loss of motor. Therefore, some modulation strategies have been employed to alleviate ZSC by suppressing zero-sequence voltage (ZSV). However, ZSC still exists in the system because ZSV can also be generated by other nonlinear factors of inverter such as the dead time of system. In addition, zero-sequence back electromotive force in the zero-sequence path can also enlarge torque ripple and ZSC. In order to deal with above problems, first, this paper proposes a full-order adaptive zero-sequence observer to estimate future ZSC and ZSV, which are able to compensate one-step control delay. Second, to achieve the maximum voltage dc bus utilization, this paper proposes a novel deadbeat predictive current control (DPCC) scheme with alternate sub-hexagonal center pulsewidth modulation strategy to suppress ZSC and torque ripple simultaneously. Finally, this paper presents a comparative study of two types of methods, namely traditional DPCC scheme and the proposed DPCC scheme. Simulation and experimental results are demonstrated to verify the effectiveness of the proposed DPCC scheme. [ABSTRACT FROM AUTHOR]

The thermal design-analysis of devices with electrical windings impregnated using varnish materials is frequently challenging. This is due to difficulties in predicting theoretically the equivalent thermal properties for the composite materials, such as impregnated windings, in a reliable manner. The existing approach makes use of the impregnation 'goodness' factor, which allows for imperfections associated with the winding impregnation to be accounted for. This, however, requires experimentally derived data from appropriate material samples or machine assemblies. Considering relatively large range of commercially available varnishes, such data are often limited and are not readily accessible. In this investigation, a set of three alternative varnish materials commonly used in impregnation of low-voltage electrical windings has been evaluated. To provide better understanding of the material impregnating properties, data from multiple impregnations are also provided. The experimental findings have been supplemented with theoretical analysis on the impregnation 'goodness' and stator-winding contact thermal resistance for a selected winding construction. To highlight the importance of appropriate input thermal material data, a case study machine design is thermally analysed. [ABSTRACT FROM AUTHOR]

The core loss is of great significance in the machine designing and optimization. In the paper, an analytical method for calculating the flux density of surface-mounted permanent magnet synchronous machine is first presented based on the equivalent magnetic circuit and conformal mapping method. The model takes into account the slotting effect and stator saturation. Subsequently, the core loss was calculated by improved Bertotti's formula in terms of the analytical model of flux density. Finally, experiment and finite element analysis (FEA) are used to validate the accuracy of the proposed analytical method for core loss. It shows that the analytical results for core loss can keep excellent agreement with the experimental results and FEA results. Therefore, the analytical model provides a great potential to the machine designing and optimization. [ABSTRACT FROM AUTHOR]

This paper describes a computationally efficient approach for mapping rotor power loss in permanent magnet (PM) machines. The PM loss mapping methodology discussed here utilizes a small number of time-step finite-element analyses (FEAs) to determine the parameters of a functional representation of loss variation with speed (frequency) and stator current and is intended for a rapid evaluation of machine performance over the entire torque–speed envelope. The research focus is placed on field-oriented-controlled brushless AC PM machines with surface-mounted PM rotor construction, although the method could be adapted for other rotor formats. The loss mapping procedure accounts for the axial segmentation of the PM array through the use of an equivalent electrical resistivity of the segmented PM array, which is obtained from three-dimensional (3-D) FEA. The PM loss can be accurately mapped across the full operational envelope, including the field-weakened mode, through a single 3-D and four two-dimensional time-step FEAs. The proposed methodology is validated on an 18-slot 16-pole surface-mounted brushless AC PM machine design. The loss mapping procedure results closely agree with the computationally demanding alternative of direct 3-D finite-element prediction of the PM power loss undertaken at each of the machine's operating points. [ABSTRACT FROM PUBLISHER]