Parameter Estimation of Asymmetrical Six-phase Induction Machines using Modified Standard Tests

In multiphase machine drives, accuracy of the estimated machine parameters is crucial for effective performance prediction and/or control. While a great amount of work has been done on parameter estimation for three-phase machines, corresponding discussions for six-phase machine remain scarce. It has been proven in the literature that the effect of mutual leakage inductance between different winding layers has a significant impact on the equivalent machine reactance, which challenges the standard separation method of stator and rotor leakage inductances from the measured locked-rotor impedance. In this paper, parameter identification of an asymmetrical six-phase induction machine using six-phase no-load and locked-rotor tests is discussed. A zero-sequence test using an improved equivalent circuit is proposed to improve the accuracy of the estimated parameters. The concept is verified using experimental results obtained from a low-power prototype asymmetrical six-phase machine

GPU Implementation of DPSO-RE Algorithm for Parameters Identification of Surface PMSM Considering VSI Nonlinearity

In this paper, an accurate parameter estimation model of surface permanent magnet synchronous machines (SPMSMs) is established by taking into account voltage-source-inverter (VSI) nonlinearity. A fast dynamic particle swarm optimization (DPSO) algorithm combined with a receptor editing (RE) strategy is proposed to explore the optimal values of parameter estimations. This combination provides an accelerated implementation on graphics processing unit (GPU), and the proposed method is, therefore, referred to as G-DPSORE. In G-DPSO-RE, a dynamic labor division strategy is incorporated into the swarms according to the designed evolutionary factor during the evolution process. Two novel modifications of the movement equation are designed to update the velocity of particles. Moreover, a chaotic-logistic-based immune RE operator is developed to facilitate the global best individual (gBest particle) to explore a potentially better region. Furthermore, a GPU parallel acceleration technique is utilized to speed up parameter estimation procedure. It has been demonstrated that the proposed method is effective for simultaneous estimation of the PMSM parameters and the disturbance voltage (Vdead) due to VSI nonlinearity from experimental data for currents and rotor speed measured with inexpensive equipment. The influence of the VSI nonlinearity on the accuracy of parameter estimation is analyzed