Application of Extreme Learning Machine in Fault Classification of Power Transformer

作     者：Athikkan Venkatasami Pitchai Latha Athikkan Venkatasami;Pitchai Latha

作者机构：Department of EEE Einstein College of Engineering Tirunelveli India Department of CSE Government College of Engineering Tirunelveli India 

出 版 物：《Circuits and Systems》 (电路与系统（英文）)

年 卷 期：2016年第7卷第10期

页      面：2837-2845页

学科分类：08[工学] 0812[工学-计算机科学与技术（可授工学、理学学位）] 

主　　题：Transformer Dissolved Gas Analysis Machine Learning 

摘      要：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.