Wavelet approach and support vector networks based power quality events recognition and categorisation

The power quality disturbances are still unsolved problems due to the increased power electronic load getting into the system. This is as a result of continuous use of non-linear loads and the faults that occur on the power system. The power quality disturbances that often occur include voltage sag, voltage swell, harmonics, transients, flicker and interruption. To detect power quality disturbances, discrete wavelet approach was adopted in the feature extraction process. For the classification of the power disturbances support vector networks was used. Synthetic power quality signals were used in this paper. In this work, Mathematical models for various PQ signal disturbances is developed and validated against real time signal. The synthetic signal was generated using synthetic parametric equations; the signal was filtered to remove unwanted noise. Events such as dip, swell and interruption were introduced to the signal. Discrete Wavelet Transform is then used for the detection of the events and change points, the signals of each event were trained using Support Vector Networks. The results obtained from the developed system show a high degree of classification rate.