Title: Augmenting WAMPAC with machine learning tools for early warning and mitigation of blackout events

Authors: Sudha Gupta; Faruk Kazi; Sushama Wagh; Navdeep Singh

Addresses: Electrical Engineering Department, Veermata Jijabai Technological Institute, Mumbai 400 019, India; K.J. Somaiya College of Engineering, Mumbai 400 077, India ' Electrical Engineering Department, Veermata Jijabai Technological Institute, Mumbai 400 019, India ' Electrical Engineering Department, Veermata Jijabai Technological Institute, Mumbai 400 019, India ' Electrical Engineering Department, Veermata Jijabai Technological Institute, Mumbai 400 019, India

Abstract: The development of phasor measurement unit (PMU) in the power network and availability of real-time communication in wide area monitoring system has enabled the proactive blackout prediction and possibility of mitigation against blackout events. The objective of this paper is to provide a wide area monitoring protection and control (WAMPAC) model which can predict cascade failure and minimise the risk of massive blackout. The proposed model is a combination of simulation and a measurement-based approach. The key contribution of this paper is a topological analysis of grid using graph theoretic approach, blackout prediction using machine learning technique and the mitigation plan against blackout by combining graph theoretic approach and change in voltage phase angle at different buses. The proposed methodology is validated using IEEE 30 bus system.

Keywords: blackout prediction; cascade failure; grid topology; neural network; NN; phasor measurement unit; PMU; phasor data concentrator; PDC; probability distribution; wide area monitoring protection and control; WAMPAC.

DOI: 10.1504/IJHT.2018.090283

International Journal of Humanitarian Technology, 2018 Vol.1 No.1, pp.83 - 100

Received: 20 Jan 2015
Accepted: 13 Sep 2015
Published online: 09 Mar 2018 *