Partial Discharge Classification Using Neural Networks and Statistical Parameters HUNG-CHENG CHEN, PO-HUNG CHEN*, and MENG-HUI WANG Department of Electrical Engineering, National Chin-Yi University of Technology, 35, Lane 215, Sec. 1, Chungshan Road, Taiping, Taichung, Taiwan. *Department of Electrical Engineering, Saint John’s University, 499, Sec. 4, Tam King Road, Tamsui, Taipei, Taiwan. Abstract: - Partial discharge (PD) pattern recognition is an important tool in high-voltage insulation diagnosis of power systems. A PD pattern classification approach of high-voltage power transformers based on a neural network is proposed in this paper. A commercial PD detector is firstly used to measure the 3-D PD patterns of epoxy resin power transformers. Then, the gray intensity histogram extracted from the raw 3-D PD patterns are statistically analyzed for the neural-network-based (NN-based) classification system. The system can quickly and stably learn to categorize input patterns and permit adaptive processes to access significant new information. To demonstrate the effectiveness of the proposed method, the classification ability is investigated on 120 sets of field tested PD patterns of epoxy resin power transformers. Different types of PD within power transformers are identified with rather encouraged results. Key-Words: - Partial Discharge, Pattern Classification, Neural Network, Statistical Parameter. 1 Introduction Power transformers play a crucial role in operation of transmission and distribution systems. A dielectric failure in a power transformer could result in unplanned outages of power systems, which affects a large number of customers [1]. Therefore, it is of great importance to detect incipient failures in power transformers as early as possible, so that they can be switched safely and improve the reliability of the power systems. Partial discharges phenomenon usually originates from insulation defects and is an important symptom to detect incipient failures in power transformers. Classification of different types of PDs is of importance for the diagnosis of the quality of HV power transformers. PD behavior can be represented in various ways. Because of the randomization of PD activity, one of the most popular representations is the statistics- based φ-Q-N distribution, i.e., the PD pattern is described using a pulse count N versus pulse height Q and phase angle φ diagram. Previous experimental results have adequately demonstrated that φ-Q-N distributions are strongly dependent upon PD sources, therefore the 3-D patterns can be used to characterize insulation defects [2]. This provides the basis for pattern recognition techniques that can identify the different types of defects. The automated recognition of PD patterns has been widely studied recently. Various pattern recognition techniques have been proposed, including expert systems [3], fuzzy clustering [4], and neural networks (NNs) [5], [6]. The expert system and fuzzy approaches require human expertise, and have been successfully applied to this field. However, there are some difficulties in acquiring knowledge and in maintaining the database. NNs can directly acquire experience from the training data, and overcome some of the shortcomings of the expert system. However, the raw values of 3-D patterns were used with the NN for PD recognition in previous studies [7], the main drawbacks are that the structure of the NN has a great number of neurons with connections, and time-consuming in training. To improve the performance, the gray intensity histogram [8] that extract relevant characteristics from the raw 3-D PD patterns are statistically analyzed for the proposed NN-based classifier. Four statistical features including skewness, kurtosis, coefficient of standard deviation, and correlation coefficient [9] are calculated based on this gray intensity histogram. The fault diagnosis database is built in accordance with the statistical features extracted. The proposed NN-based classifier can then quickly and stably learn to categorize input patterns and permit adaptive processes to access Proceedings of the 6th WSEAS International Conference on Instrumentation, Measurement, Circuits & Systems, Hangzhou, China, April 15-17, 2007 84