Computation-Efficient Decoupled Multiparameter Estimation of PMSMs From Massive Redundant Measurements

Comprehensive parameters testing and analysis are critical to high-performance modeling and control of permanent magnet synchronous machines (PMSMs). In this article, a novel decoupled approach for dual three-phase PMSM parameter estimation including winding resistance, machine inductances, and PM flux linkage is proposed for comprehensive parameter testing. An improved machine model considering magnetic saturation and inverter nonlinearity is proposed at first, in which a quadratic equation is employed to model the nonlinear variation of machine inductances and inverter voltage distortion is also modeled. Thereafter, a novel decoupled estimation model is proposed to decouple multiparameter estimation into four simplified estimations using least squares method. This decoupled model can effectively reduce the cross influences between parameters and improve the computation efficiency. Moreover, it is capable of dealing with massive redundant measurements for accurate and computation-efficient parameter estimations, which is especially suitable for obtaining machine parameters over a wide operation range during machine testing, such as inductance maps under different operating conditions.