Department of Electronics Engineering, K.J.S.C.E. Vidhyavihar Mumbai, India

Biography: Dr. Sudha Gupta received her Ph. D degree in Electrical Engineering, from VJTI Mumbai, M. E. degree in Electronics Engineering, from the VJTI Mumbai University and B.E. Degree in Electronics Engineering, from Government Engineering College Ujjain M.P India. She is a faculty and Dean of students' affairs in KJ Somaiya College of engineering Vidyavihar Mumbai India.

She has published research papers in IEEE Transaction of industrial Electronics, Springer, Elsevier journal of electrical power & energy systems, and in various international conferences. She is working as a reviewer for Transactions/Journal and International conferences. She has submitted major and minor research proposals to various funding agencies.

She has more than 22 years of UG/PG teaching experience and received best teacher award of Electronics engineering for the year 2009-10. She had served as member board of studies university of Mumbai in electronics engineering for six years (September 2006 to November 2012). Her Area of Interest is wireless communication, Image processing communication network and machine learning.

Speech Title: Blackout prediction in Smart Power Transmission System using Support Vector Machine

Abstract: Electrical power grid is one of the most complex interconnected networks mankind has ever created. It is an integration of green energy and communication technology with power system that has made the grid smart. At the same time, due to increase in the complexity of the grid, disturbances such as cascade link failure has also increased to a large extent which may lead to blackout. A smart power grid needs to become smarter in order to provide a reliable and secure supply of electricity under normal and perturbed power transmission system such as cascading link failure. To achieve this, research is focused on vulnerability analysis, prediction and post fault analysis of power network. In literature, researchers have applied considerable efforts in analysis and understanding of cascade failures in power networks. Among such efforts, the deterministic, stochastic, probabilistic and topological models have been used widely. The concept of well proven random variable, probability theory and relative entropy has been used in this paper for blackout risk analysis. Complete realization of smart grid needs high security and robustness against cascade failure which requires real time prediction. Hence, probabilistic framework further enhanced by integration of Support Vector Machine (SVM) machine learning tool. Mathematical models derived and validated with the simulation wherein bench mark IEEE 30 bus system has been used as a prototype power network. The proposed methodology is a step towards realization of wide area monitoring protection and control system of Smart Power Grid networks.