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Most of the conventional methods of power quality assesment in power systems are almost exclusively based on Fourier Transform that suffer from various inherent limitations. First limitation of an FFT based method is that of frequency resolution, whereas the second limitation is due to no coherent signal sampling of the data which proves itself as a leakage in spectrum domain. These two performance limitations of FFT or similar methods are particularly troublesome when analyzing short data records. To overcome from this problem, high regulation spectrum estimation methods can be used where resolution problem is not found. In this thesis, high resolution methods, such as MUSIC, root MUSIC and ESPRIT are discussed that use a different approach to spectral estimation; instead of trying to estimate the power spectral density (PSD) directly from the data, they model the data as the output of a linear system driven by white noise, and then attempt to estimate the parameters of that linear system. Detail Matlab simulations are carried out in order to investigate the performance of MUSIC, Root MUSIC and ESPRIT methods in estimating amplitude, power (squared amplitude) and frequency estimation of synthetic power signal both in clean and noisy conditions. Using mean square error (MSE) as the evaluation criterion, the variation of amplitude, power (squared amplitude) and frequency estimation are shown with respect to data sequence length and SNR and their influences on MSE are compared for the different methods as mentioned above.

Performance Comparison of Power Quality Evaluation Using Advanced High Resolution Spectrum Estimation Methods