Document Type
Article
Publication Date
2019
Publisher
IEEE
Source Publication
2019 North American Power Symposium (NAPS)
Source ISSN
978-1-7281-0408-9
Abstract
Although there has been notable progress in modeling cascading failures in power grids, few works included using machine learning algorithms. In this paper, cascading failures that lead to massive blackouts in power grids are predicted and classified into no, small, and large cascades using machine learning algorithms. Cascading-failure data is generated using a cascading failure simulator framework developed earlier. The data set includes the power grid operating parameters such as loading level, level of load shedding, the capacity of the failed lines, and the topological parameters such as edge betweenness centrality and the average shortest distance for numerous combinations of two transmission line failures as features. Then several machine learning algorithms are used to classify cascading failures. Further, linear regression is used to predict the number of failed transmission lines and the amount of load shedding during a cascade based on initial feature values. This data-driven technique can be used to generate cascading failure data set for any real-world power grids and hence, power-grid engineers can use this approach for cascade data generation and hence predicting vulnerabilities and enhancing robustness of the grid.
Recommended Citation
Shuvro, Rezoan Ahmed; Das, Pankaz; Hayat, Majeed M.; and Talukder, Mitun, "Predicting Cascading Failures in Power Grids using Machine Learning Algorithms" (2019). Electrical and Computer Engineering Faculty Research and Publications. 639.
https://epublications.marquette.edu/electric_fac/639
ADA Accessible Version
Comments
Accepted version. Published in the proceedings of the conference 2019 North American Power Symposium (NAPS). DOI. © 2019 The Institute of Electrical and Electronics Engineers. Used with permission.