An Influence Model Approach to Failure Cascade Prediction in Large Scale Power Systems
Name
ACC_Paper_InfluenceModel.pdf
Description
Accepted version
Size
705.98 KB
Format
Adobe PDF
Checksum (MD5)
e71c362f47c7dbd44fc6f84491e56d6a
Author(s)
Wu, X
Wu, D
Modiano, E
Date Issued
2020
Journal
Proceedings of the American Control Conference
Publisher
IEEE
Citation
Wu, X, Wu, D and Modiano, E. 2020. "An Influence Model Approach to Failure Cascade Prediction in Large Scale Power Systems." Proceedings of the American Control Conference, 2020-July.
Version
Author's final manuscript
Abstract
© 2020 AACC. Power system failures are often accompanied by failure cascades which are difficult to model and predict. The ability to predict the failure cascade is important for contingency analysis and corrective control designs to prevent large blackouts. In this paper, we study an influence model framework to predict failure cascades. A hybrid learning scheme is proposed to train the influence model from simulated failure cascade sample pools. The learning scheme firstly applies a Monte Carlo approach to quickly acquire the pairwise influences in the influence model. Then, a convex quadratic programming formulation is implemented to obtain the weight of each pairwise influence. Finally, an adaptive selection of threshold for each link is proposed to tailor the influence model to better fit different initial contingencies. We test our framework on a number of large scale power networks and verify its performance through numerical simulations. The proposed framework is capable of predicting the final state of links within 10% error rate, the link failure frequency within 0.08 absolute error, and the failure cascade size within 7% error rate expectedly. Our numerical results further show that the influence model framework can predict failure cascade two magnitudes faster than the power flow based prediction approach with a limited compromise of accuracy, making it very attractive for online monitoring and screening.
MIT Department
Massachusetts Institute of Technology. Laboratory for Information and Decision Systems
Massachusetts Institute of Technology. Department of Aeronautics and Astronautics
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Creative Commons Attribution-Noncommercial-Share Alike
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DOI of Published Version
https://doi.org/10.23919/ACC45564.2020.9147348
