Accurate Phasor Estimation During Power Swing

Accurate phasor and frequency estimation during power system oscillations using least squares

“…Therefore, a negative sequence component derived from the estimated phasors is taken as a reference for analysing the performance. The errors in the individual phasors accumulate in the negative sequence component [17]. The negative sequence component should be a minimum as power oscillation is a balanced phenomenon as depicted in the three-phase waveform in Fig.…”

“…Different non-DFT based methods are available in [12][13][14][15][16][17][18][19][20][21][22] for improved phasor estimation during dynamics with increased computational complexity. A Prony technique proposed in [12] provides accurate phasor, damping, frequency, and ROCOF information of the signal using one cycle of sample data.…”

“…Two dynamic signal models (amplitude and phase modulation) are proposed in [16] for accurate phasor estimation. Two frequencies signal model is used in [17] for accurate phasor estimation during power swing. The model parameters are estimated using least squares.…”

Modified DFT-Based Phasor Estimation Algorithms for Numerical Relaying Applications

“…3) Power Swings: Power swing is a phenomenon of oscillation in machine rotor angle often caused by disturbances, such as transmission line tripping, loss of generation and load rejection [25]. It causes modulation of voltage and current signals due to low-frequency components, causing oscillations on amplitude and phase of their phasors [26].…”

Signal processing techniques for synchrophasors considering short‐circuit signals: a comparative study

“…The discrete Fourier transform (DFT) is traditionally used in power systems, but recent propositions include modifications of DFT [12–17]. Other windowed methods propose the use of least‐squares (LS) solution [18–23], genetic algorithms [24], subspace‐based methods [25–30] and Taylor expansion with LS [31, 32]. In order to avoid time‐burden computation, recursive methods were also proposed, including recursive LS (RLS) [21, 33–35], Newton [36], Gauss–Newton methods [37, 38] and filter‐based approaches [39–43].…”