Power quality events analysis using wavelet transform - Rufus, Shirley (2012)
With an increasing usage of sensitive electronic equipment, power quality studies had grown to perform power quality data analysis. Wavelet transformation technique was founded to be more appropriate to analyze the various types of power quality events.This project compares the use of various types of wavelets at different scales and levels of decomposition on analyzing real recorded Power quality (PQ) events from Skudai 22kV distribution system. Voltage sag, voltage swell and transient event have been tested. PQ events data were extracted by RPM Power Analysis Softwarebased on a standard curve called as CBEMA curve. Background and indicators in CBEMA curve were studied, hence various PQ events were able to identify and analyze. This project also applied the 1-D WPT and 1-D SWTD of Matlab wavelet toolbox for further analysis the recorded PQ events. In 1-D WPT, four (4) types of wavelets with Shannon entropy based are applied and aim to determine the most appropriate mother wavelet for better compression and analyzed the recorded data of PQ event. Compression of voltage sag and swell waveforms were carried out with low energy loss capability was verified in order to preserve the original waveform of PQ event feature for further analysis. Performance of the 1-D WPT in compression on both voltage sag and swell events is compared based on Retain Energy (RE) and Number of Zeros (NZ) in percentage for all proposed wavelets at different scales and levels of decomposition. Ratio of energy loss per percentage of zero of compressed PQ events was also demonstrated. Compression using WT was also conducted for comparison.Presence of noise in the PQ events were de-noised using different mother wavelets at level 3 by varied three (3) types of noise structure available in 1-D SWTD. Effectiveness of using 1-D SWTD for analyzing transient, voltage sag and swell events with 3 different noise structures has been demonstrated by comparing the dispersion and distributionComparison of denoised waveform from WT was also demonstrated.