A Multiple Frequency Taylor Model-Based Dynamic Synchrophasor Estimation Algorithm

A Robust Algorithm for Real-Time Phasor and Frequency Estimation under Diverse System Conditions

“…It is proved in Appendix C that, under normal conditions, the parameter Ṕ (defined in (12)) can be obtained as follows.…”

“…To ensure the relevance of the measurements obtained from these devices for monitoring, protection, and control applications, it is necessary that the estimation algorithms used in them are accurate, robust against stray components, computationally efficient, and have low response time [1,2]. Hence, digital signal processing techniques such as discrete Fourier transform (DFT) [3][4][5][6][7][8][9][10][11], least squares (LS) [12][13][14][15], maximum likelihood [16], space vector transform [17], artificial neural networks [18], Hilbert transform [19], Stockwell transform [20], matrix pencil method [21], Kalman filters [22,23], subspace-based methods [24,25], and filter-based methods [26,27] have been proposed recently to estimate phasor and/or frequency under different operating conditions. However, many of the techniques mentioned above suffer from long response time during switching transients [9,13,20], high computational complexity [7,21,24], susceptibility to grid disturbances [12,18,22] and noise [19], lengthy observation window [7,10,11,[25]…”

“…Hence, digital signal processing techniques such as discrete Fourier transform (DFT) [3][4][5][6][7][8][9][10][11], least squares (LS) [12][13][14][15], maximum likelihood [16], space vector transform [17], artificial neural networks [18], Hilbert transform [19], Stockwell transform [20], matrix pencil method [21], Kalman filters [22,23], subspace-based methods [24,25], and filter-based methods [26,27] have been proposed recently to estimate phasor and/or frequency under different operating conditions. However, many of the techniques mentioned above suffer from long response time during switching transients [9,13,20], high computational complexity [7,21,24], susceptibility to grid disturbances [12,18,22] and noise [19], lengthy observation window [7,10,11,[25][26][27], and performance degradation due to: (i) off-nominal frequency condition [4][5][6]…”

A SVD-Based Synchrophasor Estimator for P-class PMUs with Improved Immune from Interharmonic Tones

“…Case E: Frequency deviation and OBI. The signal used in this test contains the fundamental and interharmonic components with a form of (12). The fundamental frequency f increases from 48 Hz to 52 Hz with a step of 0.1 Hz and its phase φ 1 is a random number within [−π, π].…”

“…The optimal linear filter [11] constructed the relationship between filter performance and estimation error. The dynamic SE using multiple frequency Taylor model [12] could reduce the estimation error under dynamic modulations. However, these P-class PMU algorithms do not have sufficient attenuation to the out-of-band interference (OBI) for using a short window.…”