Detection, characterization and classification of short duration voltage events using DWT and fuzzy logic

Electrical power supply is one of the most important issues to utilities, equipment manufactures and customers. Good power quality is defined in terms of voltage, current and frequency. The problems manifested in these electrical quantities may lead to disturbances in power quality. The effect of poor power quality can cause equipment for failure and breakdown, as the most of power electronic devices fail to sustain voltage variations and may stops working. Power quality disturbances must be detected and localized in order to avoid such incidents which will cause huge economic loss in industrial as well as in commercial areas. The power quality events are classified according to disturbance duration specifically, short and long duration events. Short duration voltage events namely, voltage interruption, sag and swell occur from 0.5 cycle to 1 min with respective decrease or increase in the rms voltage. This paper gives detection and characterization of these voltage events by extracting the features using discrete wavelet transform (DWT). These features are again utilized for the classification of these events using rule based fuzzy logic. This combination of DWT and fuzzy logic gives classification of power quality disturbances. Several simulations on detection and characterization by DWT are performed. Classification in case of these short duration voltage event is important. Hence this novel classification of all these voltage disturbance using fuzzy logic is presented to demonstrate the effectiveness of proposed method.