Detection of remote harmonics using SVD

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 buried in noise. However, the SVD computation is much more complex than the FFT, and requires more extensive mathematical manipulations.