Title :
A Probabilistic Classifier for Transformer Dissolved Gas Analysis With a Particle Swarm Optimizer
Author :
Richardson, Z.J. ; Fitch, J. ; Tang, W.H. ; Goulermas, J.Y. ; Wu, Q.H.
Author_Institution :
Network Eng., Coventry
fDate :
4/1/2008 12:00:00 AM
Abstract :
This paper presents a Parzen-Windows (PW)-based classifier for transformer fault diagnosis, which is able to interpret transformer dissolved gas analysis (DGA) with a probabilistic scheme. A global optimizer, particle swarm optimizer (PSO), is employed to optimize the parameters of PW to improve fault classification accuracies. First, the essential concept of PW-based classification using PSO is introduced. This probabilistic classification approach is then extended from a simple PW method to classifying fault types on the evidence of various gas ratios. The proposed approach not only allows an intuitive interpretation of the transformer diagnosis, but also provides a DGA reviewer with quantified confidence to support decision making. It can be seen from the results that both the diagnosis accuracy and computational efficiency are improved compared with a number of fault classification techniques.
Keywords :
Bayes methods; chemical analysis; fault diagnosis; particle swarm optimisation; transformers; Parzen-Windows-based classifier; particle swarm optimizer; probabilistic classifier; transformer dissolved gas analysis; transformer fault diagnosis; Decision making; Dissolved gas analysis; Fault diagnosis; Gases; IEC; Oil insulation; Particle swarm optimization; Petroleum; Power system reliability; Power transformer insulation; Bayes´ theorem; Parzen–Windows (PW); dissolved gas analysis (DGA); particle swarm optimization (PSO); transformer;
Journal_Title :
Power Delivery, IEEE Transactions on
DOI :
10.1109/TPWRD.2008.915812