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Title: A data mining transmission switching heuristic for post-contingency AC power flow violation reduction in real-world, large-scale systems
Transmission switching has proven to be a highly useful post-contingency recovery technique by allowing power system operators increased levels of control through leveraging the topology of the power system. However, transmission switching remains only implemented in limited capacity because of concerns over computational complexity, uncertainty of performance in AC systems, and scalability to real-world, large-scale systems. We propose a heuristic that uses a sophisticated guided undersampling procedure combined with logistic regression to accurately identify transmission switching actions to reduce post-contingency AC power flow violations. The proposed heuristic was tested on real-world, large-scale AC power system data and consistently identified optimal or near-optimal transmission switching actions. Because the proposed heuristic is computationally inexpensive, addresses an AC system, and is validated on real-world, large-scale data, it directly addresses the aforementioned issues regarding transmission switching implementation.  more » « less
Award ID(s):
1451036
PAR ID:
10528123
Author(s) / Creator(s):
;
Publisher / Repository:
Elsevier
Date Published:
Journal Name:
Computers & Operations Research
Volume:
160
Issue:
C
ISSN:
0305-0548
Page Range / eLocation ID:
106391
Subject(s) / Keyword(s):
Corrective transmission switching Contingency analysis Large-scale power systems Heuristics Data mining Topology Control
Format(s):
Medium: X
Sponsoring Org:
National Science Foundation
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