Harmonie detection at initialization with Kaiman filter

Harmonic estimation in power systems using an optimised adaptive Kalman filter based on PSO‐GA

“…To extract harmonics in the presence of noise and to estimate parameters of harmonics, such as amplitudes and phases, various harmonic estimation methods have been proposed in a number of research papers that include fast Fourier transform (FFT) [1], shorttime Fourier transform (STFT) [2,3], discrete wavelet transform (DWT) [4] and wavelet packet transform (WPT) [5], recursive least squares (RLS) [6][7][8], least mean squares (LMS) [9,10], Newton methods [11] and Kalman filters (KFs) [12,13]. Although these techniques show very good results in harmonic estimation, they have their weakness.…”

“…On the other hand, the KF technique has attracted widespread attention as KF can be used to estimate the magnitudes of the fundamental and harmonic components of a signal buried under noise. However, the initial choice of noise covariance matrices, which include the measurement noise covariance matrix R and the process noise covariance matrix Q, is crucial in noise rejection [13][14][15]. For the optimal choice of the noise covariance matrices, various improved KF algorithms have been applied to power system harmonic state estimation.…”

Detection of Voltage Sag using An Adaptive Extended Kalman Filter Based on Maximum Likelihood

“…However, the choice of the noise covariance matrices, which include the measurement noise covariance matrix R and the process noise covariance matrix Q, is crucial in KF algorithm [7][8][9]. For the optimal choice of the noise covariance matrices, various improved KF algorithms have been applied to power system harmonic state estimation.…”