The Fourier transform can be used for analysis of nonstationary signals, but the Fourier spectrum does not provide any time-domain information about the signal. When the time localization of the spectral components is needed, a wavelet transform giving the time-frequency representation of the signal must be used. In this paper, using wavelet analysis and neural systems as a new tool for the analysis of power system disturbances, disturbances are automatically detected, compacted, and classified. An example showing the potential of these techniques for diagnosis of actual power system disturbances is presented.

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