A Precise Calculation of Power System Frequency

Abstract

A precise digital algorithm based on Discrete Fourier Transforms (DFT) to estimate the frequency of a sinusoid with harmonics in real-time is proposed. This algorithm that we called the Smart Discrete Fourier Transforms (SDFT) smartly avoids the errors that arise when frequency deviates from the nominal frequency, and keeps all the advantages of the DFT e.g., immune to harmonics and the recursive computing can be used in SDFT. These make the SDFT more accurate than conventional DFT based techniques. In addition, this method is recursive and very easy to implement, so it is very suitable for use in real-time. We provide the simulation results compared with a conventional DFT method and second-order Prony method to validate the claimed benefits of SDFT.

Three-phase Frequency Measurement under Non-sinusoidal Conditions Using the Static Reference Frame.

“…In order to put the technique to the test under dynamic conditions close to those that can be found in real power systems, some cases has been analyzed using two different approaches [5][6][7][8][9][10][11][12][13]: i) experimental test using an arbitrary three-phase voltage generator and ii) computational test using synthesized signals.…”

A Two-Stage Algorithm to Estimate the Fundamental Frequency of Asynchronously Sampled Signals in Power Systems

“…Spectral techniques compensate for this leakage error to estimate the fundamental frequency. Orthogonal techniques [6][7][8][9][10][11][12][13][14] are another of the most popular approaches for real-time applications. In this approach, filters generate orthogonal signals and complex vectors, which are used to estimate the fundamental frequency.…”

Fundamental Frequency Estimation in Power Systems Using Complex Prony Analysis

“…Several methods for frequency estimation have been reported in the technical literature [1][2][3][4][5][6][7][8][9][10][11][12][13][14][15][16][17][18][19]. Zero-crossingbased techniques are the most widely used methods for frequency estimation [1,2].…”

“…However, its performance is prone to error in unbalanced conditions. A variety of approaches, including adaptive neural networks (ANN) [8,9], least square techniques [10][11][12], Newton-type algorithms [13], Prony estimation [14], DFTs [15][16][17], and the maximum likelihood method [18,19] have also been used to estimate the frequency of power system signals.…”