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Comparing Machine-Learning Algorithms used to Optimize Power Systems.

Colby, Jack
; 
Fortin, Ellison
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Abstract
As the integration of renewable energy resources to large-scale power systems rises, operating these systems has become increasingly challenging. Power system operation requires robust numerical solution algorithms to solve AC power flow equations. Machine-learning (ML) training algorithms, such as Adam (an ML-based numerical solver used to train GPT-3), have been implemented to solve such problems. Our research focuses on two innovations in ML training algorithms as prospective solutions: adaptive step-size tuning applied to an Adam-based solution algorithm, and the application of other ML algorithms to solve power flow. We compare results on several large-scale power system test cases.
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Date
2024-01-01
Student Status
Undergraduate
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Type of presentation
Poster Presentation
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UVM Student Research Conference
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URI
https://hdl.handle.net/20.500.14849/8729
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Advisor(s)
Sam Chevalier
Department
Program/Major
Electrical Engineering
College/School
College of Engineering and Mathematical Sciences
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Engineering and Math Science
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