Comparing Machine-Learning Algorithms used to Optimize Power Systems.
Colby, Jack Fortin, Ellison
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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Undergraduate
Date
2024-01-01