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Information fusion theory is an efficient method in partial discharge pattern recognition in recent years. As one of the most important equipments in the power system, partial discharge (PD) affects the transformer's properties in a long-term period. In order to better understand the
development of transformer partial discharge, 3 kinds of experimental models simulating discharges were designed and model experiments were performed, meanwhile, the oil-gas data and partial discharge signal were collected to analyze the variable law of the dissolved gases in oil during the
development process of the partial discharge. Then, extracting the two-dimensional operator and constructing the BP/RBF neural network to primary recognize discharge type of partial discharge in transformer. Based on this, fusion the output of neural network and oil gas features to give the
last results of pattern recognition. The finally experiments show that: information fusion have enough ability to recognize different types of partial discharge.
ADVANCED SCIENCE LETTERS is an international peer-reviewed journal with a very wide-ranging coverage, consolidates research activities in all areas of (1) Physical Sciences, (2) Biological Sciences, (3) Mathematical Sciences, (4) Engineering, (5) Computer and Information Sciences, and (6) Geosciences to publish original short communications, full research papers and timely brief (mini) reviews with authors photo and biography encompassing the basic and applied research and current developments in educational aspects of these scientific areas.