An image processing based method for power quality event identification Hussain Shareef ⇑ , Azah Mohamed, Ahmad Asrul Ibrahim Department of Electrical, Electronic and Systems Engineering, Universiti Kebangsaan Malaysia, Bangi 43600, Selangor, Malaysia Keywords: Power quality Image processing Grayscale patterns Binary images Disturbance classification abstract This paper presents a novel technique to visualize and detect various power quality disturbance events. It is based on the image processing methods known as grayscale images and binary images. Gray image cre- ated from recorded disturbance voltage waveform is first represented as a transverse wave having com- pressions and rarefactions. Then using image enhancement techniques, the unique features of the disturbance waveform are visualized. Furthermore, the patterns obtained for a pure sine signal and the signal with disturbances are compared for identification of the signal with disturbance. The decision regarding the disturbance type is made using binary image analysis techniques. Finally, to exhibit the novelty of the proposed method, a comparison is made with a conventional image processing based power quality event detection method. In addition, evaluation studies for verifying the accuracy of the method are presented. 1. Introduction Due to the stringent demands from the microelectronics indus- try nowadays, the need for improved power quality (PQ) is also increasing. Poor PQ could cause failure or malfunction of certain equipment and processes. Hence, to guarantee high quality of power and to identify PQ problems, a large number of smart power quality meters have been used in electric power systems and industrial premises. These devices capture and store a huge num- ber of the PQ disturbance events every day. However, sophisticated software and algorithms are required to analyze the captured data. In the past, PQ data was analyzed manually, which is very time consuming and also requires special expertise. Many automatic PQ disturbance identification methods have been proposed in the last few years. Some of the earliest methods that were employed in the characterization of PQ events are based on root mean square (rms), fast Fourier transform and short time Fourier transform (STFT) [1,2]. These methods are useful in provid- ing information for signals that are stationary. However, most of the PQ data captured are non-stationary and hence the techniques cannot properly track the signal dynamics. The alternative algorithm of STFT is the wavelet transform. This technique has the capability in extracting information from non- stationary signals. In [3–8] the authors utilize wavelet transform to extract the unique features for fast-changing signals such as switching transients and impulses. Even though the wavelets provide a variable window for low and high frequency currents and voltage waveforms, the performance of the one dimensional wave- let transform cannot capture some waveform variations which do not exhibit abrupt waveform discontinuities such as voltage sag [7]. To tackle this problem, two dimensional discrete wavelet trans- form (2D-DWT) was introduced in [7,8] by separating the two dimensional cyclo-stationary disturbance event in different wavelet sub-spaces. However, it is well known that all the wavelet based methods get affected because of the effect of the noises [9,10]. To improve the performance of the wavelet, an alternative technique called the S-Transform was developed. The S-transform is equiva- lent to phase corrected continuous wavelet transform. It is fully convertible from the time domain to the two dimensional frequency translation domain, and to the familiar Fourier frequency domain. Researchers [11–14] have utilized S-transform to extract features such as amplitude factor, frequency factor, etc., from the PQ distur- bance signals. In [15], the power signal disturbances in time–time transformation (TT-transform) are derived from the S-transform. TT-transform is the two dimensional time–time representation of a one dimensional time series based upon the S-transform. TT-transform helps in the interpretation of the S-transform. Based on the features extracted from the aforementioned signal processing techniques, a variety of methods has been adopted for the decision-making stage of automatic classification of PQ distur- bances. The authors in [16–18] have utilized the Support Vector Machines (SVMs) to classify the disturbance types of PQ. Similar to SVM, artificial neural network (ANN) approaches have found applications in predicting the type of PQ disturbance [19]. Fuzzy and Rule-based expert system has also been employed for the deci- sion-making step in the process of classifying PQ disturbance types [13,14,20,21,22]. ⇑ Corresponding author. E-mail addresses: shareef@eng.ukm.my (H. Shareef), azah@eng.ukm.my (A. Mohamed), asrul@eng.ukm.my (A.A. Ibrahim).