Yelong Yu

This paper proposed a nonlinear analytical model-embedded optimal design procedure for permanent-magnet synchronous motors with an external rotor. Combined with the winding inductance calculation, a refined magnetic circuit is proposed for the analytical prediction of the electromagnetic performance. The stator nonlinearity, which is significant in torque prediction, is considered by using the conception of saturation current. Then, ant colony algorithm is employed for the multi-objective optimization for the great potential in global exploring. Finally, optimal design parameters are selected from the Pareto front. The effectiveness of the proposed optimization design method is verified by finite-element analysis and experimental results of the manufactured prototype.

This paper presents a sensorless control strategy using the novel detection coil for permanent magnet synchronous motor (PMSM) with fractional slot concentrated winding (FSCW). In order to eliminate the armature component in the coil voltage, the detection coil distribution is determined based on the analysis of synchronous inductance and leakage inductance for FSCW machines. Hence, the rotor position and angular velocity are estimated from the terminal voltage of detection coil, without any knowledge of motor parameters. Both finite element analysis (FEA) results and experiments are carried out to prove the effectiveness of the proposed method at steady and transient states.

This article presents an online method to acquire the stator inductance and flux linkage for inverter-fed surface-mounted permanent magnet synchronous motors (SPMSMs) using switching state functions instead of average models. Given the switching state functions when different voltage vectors are applied to the motors, the information of inductance and flux linkage is revealed. Based on the dc bus voltage and current derivatives obtained from either hardware methods like Rogowski coil and differential circuit, or software methods such as tracking differentiator, stator inductance, and flux linkage are able to be figured out directly using the redundant or more practical methods with the help of recursive least square (RLS). Simulations and experiments are carried out to prove the effectiveness of the proposed methods, besides, dc voltage and operating speed variation as well as the impact of resistance are taken into account. Finally, compared with the conventional parameter identification based on the average models either in α – β or d – q coordinate, the proposed methods with switching state functions have remarkable advantages especially when the rotor position error exists. What needs further effort is the challenging case at very low speed, e.g., it is hard to identify flux linkage due to the small back electromotive force (EMF).

An approach to identify overall electrical parameters for interior permanent magnet synchronous motors (IPMSMs) in steady state is proposed in this article using current derivative, with which the concerns of rank deficiency can be avoided. It is found that the conventional IPMSM models under d-q frame always neglected the pulsewidth modulation (PWM) switching effect of inverter and the quantities in these models are always regarded as constant values in steady state, making it untoward to identify all motor parameters simultaneously. Instead, the switching IPMSM model is built to analyze the details of PWM, and full-rank current derivative functions during zero and active voltage vectors are given, respectively. Without any injected signals, all electrical parameters including dq -axis inductance, stator resistance, and permanent magnet flux linkage are able to be figured out in steady state by measuring the phase current derivatives. Simulations and experiments are implemented to validate the proposed approach.

This article proposes a modified deadbeat predictive current control (M-DPCC) method to improve the dynamic performance of permanent magnet synchronous motor (PMSM). Classical DPCC only predicts one control cycle of the PMSM, which restricts the dynamic performance since multiple control cycles are required for the transient state under heavy load and rated speed working condition. The proposed M-DPCC extends the single-step prediction to the entire dynamic interval in the transient state, by which the comprehensive optimization on the angle of the stator voltage vector can be implemented. Moreover, M-DPCC performed multi-step deadbeat calculation to keep the computation modest. To verify the effectiveness of the proposed M-DPCC, transient-state performance of PMSM using the M-DPCC method, the modulated model predictive control and classical DPCC are explored under a wide range of working condition. Comparative simulation and experimental results indicate that dynamic performance of the PMSM can be improved, especially under rated speed and heavy load conditions. Besides, the parameter sensitivity to inductance mismatch of the three methods is carried out. The stable operation range of the M-DPCC under inductance mismatch is larger than the other two methods. Moreover, the computation complexity of the three methods were investigated. The M-DPCC acquired less computation time than the modulated model predictive control.

This article presents an analytical model to predict the performance of eccentric surface-mounted permanent-magnet (ESPM) motor based on the nonlinear conformal mapping and reluctance network hybrid model (NCRHM). The proposed model can reveal the mutual influence of rotor eccentricity on the stator slotting and iron saturation accounting for the distortion of the equivalent current positions and air-gap path among the conformal mappings. The transformation between magnetic voltage drop of iron and equivalent current in the air region (including air-gap and slot region) is employed to consider the increased iron saturation due to the reduced air-gap length. Based on the proposed model, the electromagnetic performance including flux linkage, back electromotive force (EMF), torque, and unbalanced magnetic force (UMF) can be accurately predicted for any kind of rotor eccentricity. In addition, the NCRHM is compared with the complex permeance model (CPM) neglecting both iron saturation and the field distortion in conformal mappings, which exhibits the great advantage of NCRHM for ESPM motor. The excellent accuracy and great efficiency of the proposed model are validated by both finite-element analysis and test results.

This paper analyzes magnet defect fault signature in the surface-mounted permanent magnet motor (SPMM) using nonlinear analytical model considering the influence of magnet shape and magnetization direction. Based on the surface current method and conformal mapping technique, the magnet defect equivalent current and equivalent nonlinearity current are proposed to represent the magnet defect. Accordingly, the performance of SPMM can be obtained. The proposed model has great potential to investigate SPMM with non-uniform magnet defect and gives theoretical basis for fault diagnosis. The extra sensing coil is added to measure the voltage waveforms, which can also be predicted using the nonlinear analytical model and shows the influence of magnet defect fault. Finally, the finite element analysis and experimental results verify the high accuracy of nonlinear analytical model.

This article elaborates a novel online full parameter estimation method for permanent magnet synchronous motors (PMSMs). Usually, only two parameters can be estimated using voltage functions in the d-q frame due to the problem of rank deficiency. The α-β frame possesses the inherent advantage that full motor parameters can be estimated using voltage functions during steady and transient states. An algorithm based on recursive least squares in the α-β frame is proposed to estimate full motor parameters, including stator resistance, $d$ -axis and $q$ -axis inductances, and flux linkage simultaneously. Simulation and experimental results on the PMSM drive system are presented to verify the effectiveness of the proposed algorithm. Compared with the method in the d-q frame, the superiority of the proposed method in terms of faster convergence rate, less computational cost, and high accuracy is demonstrated.