New Windowing Technique Detection of Sags and Swells Based on Continuous S-Transform (CST) K. Daud, A. F. Abidin, N. Hamzah, H. S. Nagindar Singh Faculty of Electrical Engineering, Universiti Teknologi Mara Malaysia, Shah Alam, Malaysia kamarul@ppinang.uitm.edu.my, ahmad924@salam.uitm.edu.my, noralizah@salam.uitm.edu.my, harapajan@gmail.com ABSTRACT This paper produces a new approach for power quality analysis using a windowing technique based on Continuous S-transform (CST). This half-cycle window technique approach can detect almost correctly for initial detection of disturbances i.e. voltage sags and swells. S-transform is a time- frequency representation whose analyzing function is the product of a fixed Fourier sinusoid with a scalable, translatable window. S-transform has better time frequency and localization property than traditional and also has ability to detect the disturbance under noisy condition correctly. The excellent time-frequency resolution characteristic of the S-transform makes it the most an attractive candidate for analysis of power system disturbances signals. KEYWORDS Power quality disturbances; initial detection; windowing; Continuous S-transform 1 INTRODUCTION Power Quality Disturbances (PQD) issue has become an increased concern for electric utilities and their customers in last decades. By increasing use of solid state switching devices, non linear and power electronically switched loads, unbalanced power systems, lighting controls, computer and data processing equipment, as well as industrial plant rectifiers and inverters is resulting to poor power quality. Disturbances in quality of electric power supply is normally caused by power line disturbances such as voltage sags/swells with or without harmonics, momentary interruption, harmonic distortion, flicker, notch, spike and transients. All this disturbances causing the problems such as malfunctions, short lifetime, instabilities, failure of electrical equipments and so on. The important issues in power quality analysis are to detect correctly and classify disturbance signals automatically in a efficient manner. Using signal processing technique, various types of PQ disturbances can be detected among others, in time, frequency and time- frequency domains. Several different techniques have been used so far in the literatures to detect power quality disturbance events. The most common technique used for detecting purpose is the calculation of the root mean square (RMS) value of the voltage supply. The main advantage of this technique in terms of calculation, it is simple, fast and much sensitive in sags and swells but not able to detect during transients [8-9]. But, the drawbacks of this technique it is dependence on the size of the sample window. A small window makes the RMS parameter less relevant, as it follows the tendency of the temporal 550 International Journal of New Computer Architectures and their Applications (IJNCAA) 2(4): 550-555 The Society of Digital Information and Wireless Communications (SDIWC) 2012 (ISSN: 2220-9085)