Discrete Wavelets transform technique application & probabilistic neural network in recognition of power capacitor switchings

Abstract

With wide spread use of sensitive nonlinear electronic devices, electromagnetic transients are capable of degrading the quality of power. Utilities often switch the shunt capacitor banks to cope with sagging voltage levels, thereby generating transients, which travel into the network of end users. Capacitor switching can cause over voltage, resonance and in advert tripping of Adjustable Speed Drives (ASD) and many other sensitive electronics devices. This paper presents a method to distinguish transients arising out of isolated capacitor switching, back-to-back capacitor switching, prestrike capacitor switching, restrike capacitor switching and voltage magnification. The Discrete Wavelet Transform (DWT) of modal voltage signal is used to extract distinguishing features from the voltage waveform of these events. The detail coefficients for d1 and d5 level only, obtained from DWT are processed, mapped and given to Probabilistic Neural Network (PNN) network, which accordingly classify the events.