| Title | Predicting Cascading Failures in Power Grids using Machine Learning Algorithms |
| Publication Type | Conference Paper |
| Year of Publication | 2019 |
| Authors | Shuvro, Rezoan A., Das, Pankaz, Hayat, Majeed M., Talukder, Mitun |
| Conference Name | 2019 North American Power Symposium (NAPS) |
| Date Published | oct |
| Keywords | average shortest distance, cascade data generation, cascading failure data, cascading failure data set, cascading failure prediction, cascading failure simulator framework, Cascading Failures, composability, data-driven technique, edge betweenness centrality, failure analysis, learning (artificial intelligence), linear regression, Load modeling, load shedding, machine learning, machine learning algorithms, massive blackouts, MAT-POWER, Metrics, Monte-Carlo simulation, pattern classification, power engineering computing, power grid operating parameters, Power Grid Vulnerability Assessment, power grids, power system faults, Power system protection, power system reliability, power transmission lines, power-grid engineers, pubcrawl, real-world power grids, regression analysis, resilience, Resiliency, topological parameters, transmission line failures, Vulnerability prediction |
| 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. |
| DOI | 10.1109/NAPS46351.2019.9000379 |
| Citation Key | shuvro_predicting_2019 |
Predicting Cascading Failures in Power Grids using Machine Learning Algorithms