Abstract
This paper presents the detection and classifications of power quality disturbances using time-frequency signal analysis. The method used is based on the pattern recognition approach. It consists of parameter estimation followed classification. Based on the spectrogram time-frequency analysis, a set of signal parameters are estimated as input to a classifier network. The power quality events that are analyzed are swell, sag, interruption, harmonic, interharmonic, transient, notching and normal voltage. The parameter estimation is characterized by voltage signal in rms per unit, waveform distortion, harmonic distortion and interharmonic distortion. A rule based system is developed to detect and classify the various types of power quality disturbances. The system has been tested with 100 data for each power quality event at SNR from OdB to 50dB to verify its performance. The results show that the system gives 100 percent accuracy of power quality signals at 30 dB of SNR.
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