TITLE:
Application of Extreme Learning Machine in Fault Classification of Power Transformer
AUTHORS:
Athikkan Venkatasami, Pitchai Latha
KEYWORDS:
Transformer, Dissolved Gas Analysis, Machine Learning
JOURNAL NAME:
Circuits and Systems,
Vol.7 No.10,
August
4,
2016
ABSTRACT: Reliability of
power system is very essential for every nation to generate and transmit power
without interruption. Power transformer is one of the most significant
electrical apparatus and hence it must be kept in good health. Identification
and classification of faults in power transformer is a major research area.
Conventional method of fault classification in transformer uses gas
concentrations data and interprets them using international standards. These
standards are not able to classify the faults correctly under certain
conditions. To overcome this limitation, several soft computing tools namely
artificial neural network (ANN), Support Vector Machine (SVM) etc. are used to
automate the process of classification of faults in transformers. However,
there is a scope exists to improve the classification accuracy. Hence, this
research work focuses to design Extreme Learning Machine (ELM) method for classifying
fault very accurately using enthalpy of dissolved gas content in transformer
oil as an input feature. The ELM method is tested with two databases: one based
on IEC TC10 database (DB1) and the other one based on data collected from
utilities in India (DB2). The application of ELM to Power Transformer fault
classification based on enthalpy as input feature outperforms over the
conventional classification based on gas concentration as input feature.