Adaptive Phasor Estimation Algorithm Using Improved KFs under Steady-State/Dynamic Conditions

摘要

Devoted to balancing the performance of phasor estimation under steady-state and dynamic conditions, this paper proposes an adaptive phasor estimation algorithm based on two improved Kalman filters (KF). A moving average method is adopted to increase the signal to noise ratio (SNR) of measurements in traditional Kalman filter. Besides, a real-valued operations based Taylor-Kalman filter (RTKF) is developed to reduce the computational complexity of traditional TKF. Then, the two KFs are initialized by rough frequency estimates and updated by precise ones sequentially. The appropriate results corresponding to the current conditions are adaptively selected by a fast change detector based on residuals. Tests under steady-state and dynamic conditions demonstrate the effectiveness of the whole algorithm.