Sensorless Control of Distributed Power Generators With the Derivative-Free Nonlinear Kalman Filter

Abstract

A control method for distributed interconnected power generation units is developed. The power system comprises permanent-magnet synchronous generators (PMSGs), which are connected to each other through transformers and tie-lines. A derivative-free nonlinear Kalman filtering approach is introduced aiming at implementing sensorless control of the distributed power generators. In the proposed derivative-free Kalman filtering method, the generator's model is first subjected to a linearization transformation that is based on differential flatness theory and next state estimation is performed by applying the standard Kalman filter recursion to the linearized model. Unlike Lie algebra-based estimator design methods, the proposed approach provides estimates of the state vector of the PMSG without the need for derivatives and Jacobian calculation. Moreover, by redesigning the proposed derivative-free nonlinear Kalman filter as a disturbance observer, it is possible to estimate at the same time the nonmeasurable elements of each generator's state vector, the unknown input power (torque), and the disturbance terms induced by interarea oscillations. The efficient real-time estimation of the aggregate disturbance that affects each local generator makes possible to introduce a counterdisturbance control term, thus maintaining the power system on its nominal operating conditions.