Power System Frequency Estimation Using Adaptive Accelerated MUSIC

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

“…Let the estimated autocorrelation matrix corresponding to the previous data string be denoted by Rx old . The new autocorrelation matrix Rx new is estimated using (24) when the most recent data string is obtained. We can now create an M × M matrix ∆ Rx as shown below:…”

“…Input: signal samples, X1old , X1new Output: estimated frequency Initialization: M and L 1: take L-length of input data 2: create X f , estimate Rx new , and calculate ∆ Rx using ( 22), (24), and (35), respectively The first valid estimated frequency realized using Algorithm 1 is obtained after receiving the first L-length of input data; let this frequency be denoted by f1,L . It will be shown in Section 7.4 that the proposed iESPRIT is able to estimate the frequency precisely even when L is lower than one-cycle-length of input data (i.e., L < N), which is a significant achievement especially for protection applications.…”

“…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]…”

An Instantaneous Frequency Identification Algorithm for Time-Varying Frequency Signals

“…Still, it is hard to obtain the IF value of the component we care about when the structure of the spectrums varies with time. Moreover, the DFT based methods are affected by the picket-fence effect and aliasing effect [5], [6], [15]. When the system frequency change rate reaches a certain level, these methods will not be able to adapt to it.…”