Báo Cáo Recognition and classification of power quality disturbances using the wavelet-based neural network

ABSTRACT
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In this paper, a pattern wavelet-based neural network classifier is applied to recognize power-quality
disturbances such as harmonic distortion, capacitor switching, voltage sag/swell, momentary interruption,
over/under voltage, and flicker. The discrete wavelet transform (DWT) is combined with the probabilistic
neural network (PNN) model to construct the classifier. Firstly, the multi-resolution analysis of DWT and
Parseval’s theorem are employed to extract the energy distribution features of the distorted signal at different
resolution levels. Next, the energy curve of the given signal is calculated and a relationship between this energy
curve and the one of the corresponding fundamental component is established. Finally, the PNN classifies and
identifies the disturbance type according to the energy curve deviation. The paper shows that each power
quality disturbance has specific deviations from the pure sinusoidal waveform and this is good at recognizing of
the type of disturbance.