Power Quality Signals Classification System using Time-frequency Distribution A. R. Abdullah 1 , N. A. Abidullah 2 , N. H Shamsudin 3 , N. H. H. Ahmad 4 , M. H. Jopri 5 1, 2, 3, 4,5 Faculty of Electrical Engineering, Universiti Teknikal Malaysia Melaka, Malaysia 1 abdulr@utem.edu.my, 2 athira_abidullah@yahoo.com, 3 nurhazahsha@utem.edu.my, 4 fizah_jaa@yahoo.com, 5 hatta@utem.edu.my, Keywords: Power quality, time-frequency distribution and spectrogram Abstract. Power quality signals are an important issue to electricity consumers. The signals will affect manufacturing process, malfunction of equipment and economic losses. Thus, an automated monitoring system is required to identify and classify the signals for diagnosis purposes. This paper presents the development of power quality signals classification system using time-frequency analysis technique which is spectrogram. From the time-frequency representation (TFR), parameters of the signal are estimated to identify the characteristics of the signals. The signal parameters are instantaneous of RMS voltage, RMS fundamental voltage, total waveform distortion, total harmonic distortion and total non harmonic distortion. In this paper, major power quality signals are focused based on IEEE Std. 1159-2009 such as swell, sag, interruption, harmonic, interharmonic, and transient. An automated signal classification system using spectrogram is developed to identify, classify as well as provide the information of the signal. Introduction The quality of power system has become an important issue to electricity users at all levels of usage. The ability to maintain voltage and current signals with constant amplitude and constant fundamental frequency presents the quality of electrical power supplied to the customers [1]. The power quality signals can cause failure or disoperation of equipment and economic problem. Thus, an real-time power quality signals detection and classification system is needed in order to provide adequate coverage of the entire system, understand the causes of these signals, resolve existing problems and predict future problems [2]. Prompt and accurate diagnosis of signals will ensure quality of power, reduce the risk of interruptions by reducing the time to diagnose and rectify failures [3]. The proper diagnosis of power quality signals requires a high level of engineering expertise [4]. The diagnosis of power quality required expert knowledge in many areas of electric power such as transformers, power electronics, power supplies, protection, power system faults, harmonics, signal analysis, measuring instruments, and general power systems operation [5]. The poor power quality can cause reduction of the lifetime of the load, the bad working of protection devices, instabilities, interruptions in production and significant costs in lost production and downtime [6]. Many techniques were presented by various researchers for classifying power quality signals. The most widely used is in signal processing is spectral analysis using Fourier analysis which is Fourier transform. The Fourier transform is powerful technique for stationary signal because the characteristics of the signal not change with time but it not useful for non stationary signal because is inadequate to track the changes in the magnitude, frequency or phase [7]. The time-frequency representation is introduced for overcome the limitation of this technique. There are numerous of time frequency distributions technique which is wavelet transform, short time Fourier transform (STFT), Gabor transform, S-transform, and spectrogram [7,8]. However, this paper focuses on time a frequency analysis technique which is spectrogram to identify the signals in time frequency domain. Applied Mechanics and Materials Vols. 494-495 (2014) pp 1889-1894 © (2014) Trans Tech Publications, Switzerland doi:10.4028/www.scientific.net/AMM.494-495.1889 All rights reserved. No part of contents of this paper may be reproduced or transmitted in any form or by any means without the written permission of TTP, www.ttp.net. (ID: 175.145.214.162-25/01/14,15:10:13)