Detection, localization, and classification of power quality disturbances using discrete wavelet transform technique

Modern spectral and harmonic analysis is based on Fourier based transforms. However, these techniques are less efficient in tracking the signal dynamics for transient disturbances. Consequently, The wavelet transform has been introduced as an adaptable technique for non-stationary signal analysis. Although the application of wavelets in the area of power engineering is still relatively new, it is evolving very rapidly. The application of the wavelet transform in detection, time localization, and classification of power quality disturbances is investigated and a new identification procedure is presented. Different power quality disturbances will be classified by a unique energy distribution pattern based on the difference of the discrete wavelet coefficients of the analyzed signal and a pure sine wave. Verification of the proposed algorithm was done by simulating different disturbances and analyzing the results.