Selection of Most Relevant Input Parameters Using Principle Component Analysis for Extreme Learning Machine Based Power Transformer Fault Diagnosis Model

Abstract

The dissolved gas-in-oil analysis is a prevailing methodology being extensively utilized to diagnose incipient faults in oil-immersed power transformers. However distinct approaches have been implemented to find out dissolved gas analysis (DGA) results, they may sometimes fail to diagnose precisely. The incipient fault identification accuracy of various artificial intelligence (AI)-based methodology is assorted with change of input parameters. Thus, selection of input variable to an AI model is major research area. In this paper, principle component analysis algorithm using RapidMiner is applied to 360 experimental datasets, imitated in lab to identify most pertinent input variables for incipient fault classification. Thereafter, multi-class Extreme Learning Machine (ELM) technique is implemented to classify the incipient faults of power transformer and its performance is compared with artificial neural network, gene expression programming, fuzzy-logic, and support vector machine. The compared result shows that ELM provides better diagnosis results up to 100% accuracy at proposed input variable in short of time period which is helpful in on-line condition monitoring.

Keywords::

• DGA
• fault diagnosis
• ELM
• PCA
• power transformer

Additional information

Notes on contributors

Hasmat Malik

Hasmat Malik was born in Delhi, India, in 1983. He received pre-engineering qualification from the Govt. of NCT of Delhi and Post-engineering qualification from National Institute of Technology, Hamirpur, Himachal Pradesh, India. He is currently an assistant professor in the Division of Instrumentation & Control Engineering, Netaji Subhas Institute of Technology, Delhi University, New Delhi, India. His current research interest includes soft computing applications to condition monitoring, fault diagnosis, signal processing, power quality, renewable energy, and microgrids.

Sukumar Mishra

Sukumar Mishra (M’97–SM’04) received his B.E. degree from the University College of Engineering, Burla, Orissa, India, and his M.E. and Ph.D. degrees from Regional Engineering College, Rourkela, Orissa, India, in 1990, 1992, and 2000, respectively. In 1992, he joined the Department of Electrical Engineering, University College of Engineering, Burla, as a lecturer, and subsequently became a Reader in 2001. Presently, he is a professor in the Department of Electrical Engineering, Indian Institute of Technology, New Delhi, India. Dr. Mishra has been honored with many prestigious awards such as the INSA Young Scientist Medal in 2002, the INAE Young Engineers Award in 2002, and recognition as the DST Young Scientist in 2001 to 2002. He is a Fellow of the Indian National Academy of Engineering, the Institute of Engineering and Technology (IET), London, U.K., and the Institute of Electronics and Communication Engineering, India. His interests are in soft computing applications to power system control, power quality, renewable energy, and microgrids.