B. KARTHIKEYAN
School of EEE, SASTRA, Deemed University, Thanjavur 613 402, India
S. GOPAL
W.S. Test Systems Limited, 27th kM, Bellary Road, Doddajala Post, Bangalore 562 157, India
S. VENKATESH
Department of EEE, SRMIST, Deemed University, Kancheepuram Dist., India

Received 9 June 2005
Accepted 8 October 2005

The quality of electrical insulation of any power apparatus is an indispensable requirement for its successful and reliable operation. Partial Discharge (PD) phenomenon serves as an effective Non Destructive Testing (NDT) technique and provides an index on the quality of the insulation. The innovative trend of using Artificial Neural Network (ANN) towards the classification of PD patterns is cogent and discernible. In this paper a novel method for the classification of the PD patterns using the original Probabilistic Neural Network (PNN) as well as its variation is elucidated. A preprocessing scheme that extracts pertinent features of PD from the raw data towards achieving a compact ANN has been employed. The classification of single-type insulation defects such as voids, surface discharges and corona has been taken up. The first part of the paper gives a brief on PD, various diagnostic techniques and interpretation. The second part deals with the theoretical concepts of PNN and its adaptive version. The last part provides details on various results and comparisons of the PNN and its adaptive version in PD pattern classification.

Partial Discharge (PD); Probabilistic Neural Network (PNN); smoothing parameter; Bayes strategy; Back Propagation Algorithm (BPA); Adaptive Probabilistic Neural Network (APNN)