Augmenting WAMPAC with machine learning tools for early warning and mitigation of blackout events. (2018)

• Record Type: Journal Article
• Authors: Gupta, Sudha; Kazi, Faruk; Wagh, Sushama; Singh, Navdeep
• Is Part Of: International journal of humanitarian technology. Volume 1:Number 1(2018)
• Page Start: 83
• Page End: 100
• Publication Date: 2018
• Subjects: 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
• Languages: English
• ISSNs: 2056-6549

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.