PMSG sensorless control with the use of the Derivative-free nonlinear Kalman Filter

In the design of nonlinear controllers for power generators it is important to estimate the non-measurable state variables for generating the feedback control signal. A derivative-free nonlinear Kalman Filtering approach is introduced aiming at implementing sensorless control of the Permanent Magnet Synchronous Generator (PMSG). In the proposed derivative-free Kalman Filtering method the system is first subject to a linearization transformation that is based on the differential flatness theory and next state estimation is performed by applying the standard Kalman Filter recursion to the linearized model. Unlike the Lie algebra-based estimator design method, the proposed approach provides estimates of the state vector of the permanent magnet synchronous generator without the need for derivatives and Jacobians calculation. By avoiding linearization approximations, the proposed filtering method improves the accuracy of estimation of the system state variables, and results in smooth control signal variations and in minimization of the tracking error of the associated control loop.