Abstract:The paper examines singular value decomposition (SVD) for the 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 the superiority of SVD for signals buried in the noise. However, the SVD computation is much more complex than FFT and requires more extensive mathematical manipulations.

Abstract:The paper examines singular value decomposition (SVD) for the 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 the superiority of SVD for signals buried in the noise. However, the SVD computation is much more complex than FFT and requires more extensive mathematical manipulations.

No abstract

The paper examines the singular value decomposition (SVD) for detection of remote harmonics in signals, in the presence of high noise contaminating the measured waveform. When the number of harmonics is very large and at the same time certain harmonics are distant from the other, the conventional frequency detecting methods are not satisfactory. The methods developed for locating the frequencies as closely spaced sinusoidal signals are appropriate tools for the investigation of power system signals containing harmonics differing significantly in their multiplicity. The SVD methods are ideal tools for such cases. To investigate the methods several experiments have been performed. For comparison, similar experiments have been repeated using the FFT with the same number of samples and sampling period. The comparison has proved an absolute superiority of the SVD for signals burried in noise. However, the SVD computation is much more complex than the FFT, and requires more extensive mathematical manipulations.

No abstract