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Abstract
This article presents an extreme learning machine based fast and accurate adaptive distance relaying scheme for transmission lines in the presence of a static synchronous series compensator. The ideal trip characteristics of the distance relay is greatly affected by pre-fault system conditions, ground fault resistance, and zero-sequence voltage. The proposed research develops an extreme learning machine based adaptive distance relaying scheme for two-terminal transmission networks with static synchronous series compensators when a single-line-to-ground fault situation is most likely to occur. The study includes an analytical approach, including a steady-state model of static synchronous series compensator with detailed simulation on MATLAB/Simulink (The MathWorks, Natick, Massachusetts, USA) and open real-time simulation software with MATLAB (OPAL-RT) platform (OPAL-RT Technologies, Montreal, Quebec, Canada). The proposed extreme learning machine based adaptive distance relaying scheme is extensively validated on the two terminal transmission lines with static synchronous series compensators, and the performance is compared with the existing radial basis feed-forward neural network based adaptive distance relaying scheme. The results on simulation and real-time platform show significant improvements in the performance indices, such as speed, selectivity, and reliability of the digital relay.
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Notes on contributors
Rahul Dubey
Rahul Dubey received his master's degree in technology (M.Tech.) in the Department of Electrical Engineering, National Institute of Technology Rourkela, Orissa, India, in 2012. He is currently working toward his Ph.D. in power system engineering at the Department of Electrical Engineering, Indian Institute of Technology (IIT), New Delhi, India. He is the recipient of the prestigious 2014 Prime Minister's Research Fellows in the area of power systems and the 2015 Clayton Griffin Best Student Research Paper Award at Georgia Tech (USA). His research interest includes intelligent protection, digital signal processing, soft computing, FACTs, and wide area measurement systems (WAMS).
Subhransu Ranjan Samantaray
Subhransu Ranjan Samantaray received the B.Tech. in electrical engineering from University College of Engineering Burla, India, in 1999 and his Ph.D. in power system engineering from the Department of Electronics and Communication Engineering, National Institute of Technology, Rourkela, India, in 2007. He is an assistant professor in the School of Electrical Sciences, IIT Bhubaneswar, India. He visited the Department of Electrical and Computer Engineering, McGill University, Montréal, Canada, as a post-doctoral research fellow and visiting professor. He is the recipient of the 2007 Orissa Bigyan Academy Young Scientists Award, 2008 Indian National Academy of Engineering Best Ph.D. Thesis Award, 2009 Institute of Engineers (India) Young Engineers Award, 2010 Samanta Chandra Sekhar Award, 2012 IEEE PES Technical Committee Prize Paper Award, 2013 Excellence in Reviewing Award, 2015 Elsevier Science, and 2015 National Academy of Science India (NASI) SCOPUS Young Scientists Award. He is an editor of IET, Generation, Transmission & Distribution, Canadian Journal of Electrical and Computer Engineering, and Electric Power Components and Systems. In 2015, he became a member of NASI. His major research interests include intelligent protection for transmission systems (including FACTs) and microgrid protection with distributed generation and dynamic security assessment in large power networks.
Bijay Ketan Panigrahi
Bijay Ketan Panigrahi is an associate professor with the Department of Electrical Engineering, IIT, New Delhi. Prior to joining IIT Delhi, he was a lecturer at the University College of Engineering, Burla, Sambalpur, Orissa, for 13 years. He is the recipient of the 2004 Orissa Bigyan Academy Young Scientists Award and the Institution of Engineers (India) Institution Award for the Technical Paper (2005). He is an editor-in-chief of International Journal of Power and Energy Conversion and an editor of Neurocomputing. In 2014, he became a fellow of Indian National Academy of Engineering India. His research interests are in the areas of intelligent control of FACTS devices, application of advanced digital signal processing (DSP) techniques for power quality assessment, and application of soft-computing techniques to power system operation and control.
Vijendran G. Venkoparao
Vijendran G. Venkoparao received his Bachelors and Masters in Physics from Madurai University India in 1987 and 1989 and his doctoral degree in spatial information systems from Jawaharlal Nehru Technological University Hyderabad in 1997. His research thesis was studying the equilibrium properties of spin glass materials and extending the analogy to Neural Networks in the theoretical analysis of memory capacity using Spin glass models. He is currently the India head of Bosch Corporate Research in Bangalore, India. His major research interests are in image processing computer vision. He was a research scientist at Indian Space Research Organization (ISRO) mainly working in the areas of Ariel and satellite image analysis and later with KLA Tencor working in the areas of analyzing Scanning Electron Microscope (SEM) images for semiconductor yield management. His research interest includes neural networks, computer vision and image processing and data analytics.