Design of Adaptive Damping Controller With Wide-Area Measurements Considering Unknown Power System Dynamics

Low damping inter-area oscillations (IAOs) can jeopardize the power system stability if left unaddressed. This paper introduces an adaptive wide-area damping controller (WADC) utilizing supplementary input through converters of wind turbine system (WTS). In practical power system, simultaneously obtaining all system parameters is an infeasible task. Hence, the system identification is carried out between input location and output feedback signals using autoregressive exogenous (ARX) technique. In addition, a decentralized dynamic state estimation based on extended Kalman filter (EKF) is leveraged to estimate the feedback signals using classical model of the synchronous generators (SGs) and phasor data collected from the generator buses. Using the identified system models and estimated feedback signals, modal linear quadratic Gaussian (MLQG) controllers are designed for different scenarios like change in operating point and time delay in feedback signals. Finally, a modified interacting multiple model (IMM) strategy is employed for adaptive gain scheduling in order to ensure robust damping performance of the proposed WADC. The suggested strategy is verified on IEEE benchmark 4-machine, 11-bus system and 16-machine, 68-bus system. The results of these comprehensive case studies demonstrate that the proposed modified IMM based WADC strategy to damp IAOs is robust to operating scenarios, even with unknown power system dynamics.