Analysis Of Partial Discharge Using Phase-Resolved (Φ-Q) And (Φ-N) Statistical Techniques Namrata Bhosale, Priyanka Kothoke, Amol Deshpande, Dr. Alice N. Cheeran Department of Electrical Engineering, VJTI Abstract Partial discharges (PDs) in high-voltage (HV) insulating systems originate from various local defects, which further results in degradation of insulation and reduction in life span of equipment. In order to ensure reliable and durable operation of HV equipment, it is vital to relate the observable statistical characteristics of PDs to the properties of the defect and ultimately to determine the type of the defect. In this work, we have obtained and analyzed phase- resolved discharge patterns using parameters such as skewness and kurtosis. Keywords: Partial Discharge, Phase-resolved, Statistical parameters. 1. Introduction A PD is generally thought of as a highly localized or confined electrical discharge within an insulating medium between two conductors, and in some cases PD is the precursor to a complete electrical breakdown or fault. The occurrence of PD can be the cause of electrically-induced aging of insulating materials, for example, by formation of corrosive gaseous byproducts, erosion, sputtering, and 'tree' formation. PD, despite its localized nature, is an enormously complex phenomenon that often exhibits chaotic, non-stationary, or fractal type behaviour[1]. The PD data consist of a time sequence of charge pulses which can be represented in various ways. One possibility is the phase angle φ of the ac voltage at which the pulse of strength q is detected. This gives a phase resolved partial discharge (PRPD) pattern. A statistical description of the data is obtained by averaging over a large number of ac periods. The 2- D distribution (φ-q) represents a pattern containing information about the nature of the defect[2]. One of the important objectives of PD test is to discriminate different type of PD sources. Different types of insulation defects produce different discharge patterns. PD measurement often provides a means for detecting defects that could lead to the breakdown of the dielectric. Advancements in computer measurement techniques have made it convenient and faster to process a large amount of information and to transform this information into an understandable output[3]. In this work we process a data to calculate various statistical parameters from different discharge pulses detected during the measurement period. Different types of patterns can be used for identification of source of PD. These different patterns can be presented in terms of statistical parameters and may make it possible to identify the defect type. As each defect has its own particular degradation mechanism, it is important to know the correlation between discharge patterns and the kind of defect. Therefore, progress in the recognition of internal discharge and their correlation with the kind of defect is gaining importance in the quality control of insulating systems[4]. Various researches have been carried out in recognition of partial discharge sources using statistical techniques and neural network. In our study, we have tested various internal and external discharges like void, surface and corona using statistical parameters such as skewness and kurtosis in phase resolved pattern (φ-q) and (φ-n) and classified the partial discharge source for unknown partial discharge data. 2. Statistical Parameters The important parameters to characterize PDs are phase angle „φ’, PD charge magnitude „q’ and number of PD pulses „n’. PD distribution patterns are composed of these three parameters. Statistical parameters are obtained for phase resolved pattern. 2.1. Processing of data (φ, q and n) The data to be processed obtained from generator includes φ, q, n and voltage V. From this data, phase resolved patterns are obtained. PD pulses are grouped by their phase angle with respect to the 50 (± 5) Hz sine wave. Consequently, the voltage cycle is divided into phase windows representing the phase angle axis (0 to 360‟). If the observations are made for several voltage cycles, the statistical distribution of 1960 International Journal of Engineering Research & Technology (IJERT) Vol. 2 Issue 5, May - 2013 ISSN: 2278-0181 www.ijert.org