To improve the assessment capability of power transformers, this paper proposes a new intelligent decision support system based on fuzzy learning vector quantization (FLVQ) networks. In constructing the system, a fuzzy-based classifier is designed to divide the historical data for dissolved gas analysis (DGA) into various categories with different levels of gas attributes. For each category of gas attributes, a learning vector quantization (LVQ) network is trained to be responsible for the classification of the potential faults due to insulation deterioration. The assessment approach has been tested on the DGA data from Taiwan Power Company (TPC) and compared with the previous fuzzy diagnosis system and the existing multi-layered back-propagation based artificial neural networks (BPANN) methods. Remarkable classification accuracy and far less training efforts of the proposed approach are achieved in this paper.
|Number of pages||7|
|Journal||IEEE Transactions on Dielectrics and Electrical Insulation|
|Publication status||Published - 2001 Feb|
All Science Journal Classification (ASJC) codes
- Electrical and Electronic Engineering