A PARTIAL DISCHARGE BASED DEFECT-DIAGNOSIS SYSTEM FOR CAST-RESIN CURRENT TRANSFORMERS

摘要

This paper presents an application of an artificial neural network to partial discharge (PD) recognition in current transformers. The research contents of interest encompass the practical measurements and recognition of partial discharge signals. Firstly, to yield four experimental models of partial discharge for testing, we make use of cast-resin current transformers tailor-made with insulating defects. Then, using a commercial partial discharge detector, practical measurements of 3D patterns for the above experimental models are performed in a magnetically shielded laboratory. The 3D patterns obtained from discharge measurements and after appropriately pre-processed are used for the training of a back-propagation neural network (BPN), used as a partial discharge based defect-diagnosis system. Finally, with a view to exploring applicability in the field, this study randomly selects different levels of noise to distort the original measurements. These distorted data sets are entered for testing. Research results show that, with 20% noise per discharge count, an 80% successful recognition rate is achieved.