Browse By Repository:

 
 
 
   

Bilinear Time-Frequency Analysis Techniques for Power Quality Signals

Abdullah, Abdul Rahim and Sha'ameri, Ahmad Zuri and Mohd Said, Nurul Ain and Mohd Saad, Norhashimah and Jidin, Auzani (2012) Bilinear Time-Frequency Analysis Techniques for Power Quality Signals. In: International MultiConference of Engineers and Computer Scientists 2012 (IMECS 2012)., 14-16 March 2012, hong kong.

[img] PDF
IMECS2012_pp991-995.pdf

Download (730Kb)

Abstract

Bilinear time-frequency distributions (TFDs) are powerful techniques that offer good time and frequency resolution of time-frequency representation (TFR). It is very appropriate to analyze power quality signals which consist of non-stationary and multi-frequency components. However, the TFDs suffer from interference because of cross-terms. This paper presents the analysis of power quality signals using bilinear TFDs. The chosen TFDs are smooth-windowed Wigner-Ville distribution (SWWVD), Choi-Williams distribution (CWD), B-distribution (BD) and modified Bdistribution (MBD). The power quality signals focused are swell, sag, interruption, harmonic, interharmonic and transient based on IEEE Std. 1159-2009. To identify and verify the TFDs that operated at optimal kernel parameters, a set of performance measures are defined and used to compare the TFRs. The performance measures are main-lobe width (MLW), peak-to-side lobe ratio (PSLR), signal-to-cross-terms ratio (SCR) and absolute percentage error (APE). The result shows that SWWVD is the best bilinear TFD and appropriate for power quality signal analysis.

Item Type: Conference or Workshop Item (Paper)
Subjects: T Technology > TK Electrical engineering. Electronics Nuclear engineering
Divisions: Faculty of Electrical Engineering > Department of Power Electronics & Drives
Depositing User: Dr Abdul Rahim Abdullah
Date Deposited: 20 Jul 2012 03:18
Last Modified: 28 May 2015 03:25
URI: http://eprints.utem.edu.my/id/eprint/4478

Actions (login required)

View Item View Item

Downloads

Downloads per month over past year