Recognition of partial discharge in stator winding models based on 3-dimensionl pattern using artificial neural networks

摘要

Partial discharge (PD) is an important phenomenon and has close relation to insulation condition of electrical apparatus. Usually PD accelerates insulation deterioration and before final breakdown their activities will be much more serious than that in ordinary time. Therefore PD is an adequate characteristic quantity to inspect the insulation condition to avoid sudden failure especially for on-line monitoring. An artificial neural network (ANN) group with back propagation algorithm was developed to identify the types and extent levels of PDs. Six different physical models, which could reflect PDs in stator windings of large electrical machines, were made. Simulated PD types included surface discharge at endwinding, slot discharge, delamination in three different positions of ground wall insulation as well as a standard PD level of new machines. Different levels of voltage were applied to models to obtain various extents of PD activities. The fingerprints of experimental PD data were extracted with the Φ -q-n 3-dimensional pattern. The recognition ability of the ANN group was investigated. Different types and extents of discharge within winding insulation of large electrical machines were identified with a satisfactory recognition rate.