Power system harmonics estimation using linear least squares method and SVD

The paper examines the singular value decomposition (SVD) for estimation of harmonics in signals, in the presence of high noise. The proposed approach results in a linear least squares method. The methods developed for locating the frequencies as closely spaced sinusoidal signals are appropriate tools for the investigation of power system signals containing harmonics and interharmonics differing significantly in their multiplicity. The SVD approach is a numerical algorithm to calculate the linear least squares solution. The methods can also be applied for frequency estimation of heavy distorted periodical signals. To investigate the methods several experiments have been performed using simulated signals and the waveforms of a frequency converter current. For comparison, similar experiments have been repeated using the FFT with the same number of samples and sampling period. The comparison has proved superiority of the SVD for signals buried in the noise. However, the SVD computation is much more complex than FFT, and requires more extensive mathematical manipulations