Determination of Power Quality Events Using DWT and DTCWT Wavelets

Abstract

This chaoter evaluate PQ event algorithm considering dual tree wavelets and the results are compared with wavelets. Power quality disturbances (PQ) are generated with the growth of nonlinear loads, such as solid-state switching equipment, electronically switched devices, industrial rectifiers, and inverters. Warped voltage waveforms adversely affect electronic devices, such as electrical system failures, disk crashes, and microcontroller failures It is shown that the shift invariant property of dual tree wavelets is useful for classifying events in a variety of PQ signals with non-stationary occurrences. The energy levels of the Dual Tree Complex Wavelet Transform (DTCWT) are able to distinguish between many events as well as various sags, swells, harmonics, interrupts, and flickers.  The classification accuracy using DTCWT energy bands is improved by more than 90%. DTCWT filters selected in this paper are suitable for PQ event detection as well as classification. The input PQ signal with PQ event such as sag, swell, transient, harmonics and flicker occur at random intervals in real time. The feature detection algorithm needs to detect the presence of event from the features detected from sub bands and also characterize the event by providing information on time of occurrence, event duration, intensity and gradient.