2022
DOI: 10.48550/arxiv.2203.07505
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Closing the Loop: A Framework for Trustworthy Machine Learning in Power Systems

Abstract: Deep decarbonization of the energy sector will require massive penetration of stochastic renewable energy resources and an enormous amount of grid asset coordination; this represents a challenging paradigm for the power system operators who are tasked with maintaining grid stability and security in the face of such changes. With its ability to learn from complex datasets and provide predictive solutions on fast timescales, machine learning (ML) is well-posed to help overcome these challenges as power systems t… Show more

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Cited by 2 publications
(5 citation statements)
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“…Finally, [98,99,100] let operators explore interactively and iteratively historical explainable factors across similar situations and decisions for augmenting and keeping up-to-date the system knowledge and proper labels. This should all be carefully developed within a trustworthy framework [101]. This is an illustrative sample of today's AI potential [102,103] to provide effective assistance functions and interactions which needs to be developed further.…”
Section: Power System Ai Modules For Assistant Functionsmentioning
confidence: 99%
“…Finally, [98,99,100] let operators explore interactively and iteratively historical explainable factors across similar situations and decisions for augmenting and keeping up-to-date the system knowledge and proper labels. This should all be carefully developed within a trustworthy framework [101]. This is an illustrative sample of today's AI potential [102,103] to provide effective assistance functions and interactions which needs to be developed further.…”
Section: Power System Ai Modules For Assistant Functionsmentioning
confidence: 99%
“…For instance, Yan [22] uses entropy as a metric to generate 'relevant' samples closer to the decision boundary. [24] uses 'directed walk' methods to samples around the decision boundary. The third type of approach, generic sampling, generates points uniformly distributed in the feasible space to explore all possible OCs.…”
Section: Sampling Approachesmentioning
confidence: 99%
“…Thus, a first point of comparison to generate pre-fault OCs is with methods that consider the OPF to generate initial OCs. The other approaches in the literature explore the entire feasible space in a generic way via random sampling, often using the Latin Hypercube sampling to generate initial OCs [37,39,40,24,22]. As a consequence, a second and more pivotal comparison is with those methods that aim to uniformly cover the search space using techniques like the Latin Hypercube sampling.…”
Section: Sampling Approachesmentioning
confidence: 99%
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