Multiwavelet transform based classification of PQ events

Wavelets have been used extensively in the recent past. The work presented uses multiwavelet because of its inherent property to resolve the signal better than all single wavelets. Multiwavelets are based on more than one scaling function. The resolution is better compared to Daubechies (D4) and hence the event can be detected from lesser number of samples as compared to Daubechies (D4). The proposed methodology utilizes an enhanced resolving capability of multiwavelet to recognize power system disturbances. DempsterShafer (DS), an intelligent classifier, has been implemented and tested for various PQ events. Two sub-classifiers, heuristic classifier and statistical classifier (?2 distribution), have been used to support and strengthen the structural identification and temporal distribution factor of DS classifier. Results on various PQ events, such as voltage sag, voltage swell, outage, interruption, impulsive-transient (IT), oscillatory-transient (OT), noise, and notching, show that multiwavelets can detect and classify different PQ events efficiently and consistently. Copyright (c) 2011 John Wiley & Sons, Ltd.