This paper introduces an improved method for classifying Partial Discharge (PD) patterns using Ensemble Neural Network (ENN) learning. The method is based on training several Neural Network (NN) models and combining their predictions. In this paper it is applied to the recognition of PD from artificially created poly-ethylene-terephthalate (PET) voids and in particular the ability of the ENN to categorise statistical F-q-n patterns for two different void sizes over 50 and 250 power cycles. The training data for the ENN comprises statistical parameters obtained from voids of 0.6mm and 1mm diameter.
|Title of host publication||Proceedings of the 2011 IEEE Conference on Electrical Insulation and Dielectric Phenomena (CEIDP 2011)|
|Number of pages||4|
|Publication status||Published - 2011|
- partial discharge
- neural networks
- pattern classification