APPLICATION OF EKF BASED DYNAMIC STATE ESTIMATION FOR DFIG ROTOR POWER CONTROL UNDER FAULTY CURRENT SENSOR MEASUREMENTS

Ever increasing demand of energy and limited availability of fossil fuel have encouraged significant penetration of renewable energy sources. The doubly fed induction generator (DFIG) based wind power has emerged as one of the substantial alternative among renewable energy sources, due to controllability of electrical power over wide wind speed range. Control circuits of DFIG emphatically rely on the feedback from voltage, current measurements and the DFIG performance is likely to be affected by the sensor failure, sensor dynamic characteristics, and their interface circuits. If current measurement issue / sensor faults are not detected timely, then it can lead to generator disconnection from grid. To overcome unforeseen shutdown and loss of revenue, it is imperative to have sensor fault detection and back-up arrangement. Present work investigates effect of rotor current sensor failure on DFIG operation and proposes use of dynamic state estimation (DSE) to overcome adversarial effect of faulty current sensor. Dynamic states of DFIG are estimated using extended Kalman filter (EKF) algorithm. Non-measurable dynamic states viz. direct axis stator and rotor currents (
,
) and quadrature axis stator and rotor currents (
,
) of DFIG are estimated using active power (P) and reactive power () as measurement input to EKF algorithm. Use of dynamically estimated states as a back-up to measurements of rotor control circuit is proposed under the conditions of faulty current sensors like noisy measurement output, measurement with outliers or measurement unavailability to provide continuous control. Simulation results of proposed approach is presented on DFIG based single-machine-infinite-bus (SMIB) system using MATLAB/Simulink platform.