Matches in SemOpenAlex for { <https://semopenalex.org/work/W2991019969> ?p ?o ?g. }
Showing items 1 to 91 of
91
with 100 items per page.
- W2991019969 endingPage "106547" @default.
- W2991019969 startingPage "106547" @default.
- W2991019969 abstract "Abstract Recent trends in power systems and those envisioned for the next few decades push Transmission System Operators to develop probabilistic approaches to risk estimation. However, current methods to solve AC power flows are too slow to fully attain this objective. Thus we propose a novel artificial neural network architecture that achieves a more suitable balance between computational speed and accuracy in this context. Improving on our previous work on Graph Neural Solver for Power System [1], our architecture is based on Graph Neural Networks and allows for fast and parallel computations. It learns to perform a power flow computation by directly minimizing the violation of Kirchhoff’s law at each bus during training. Unlike previous approaches, our graph neural solver learns by itself and does not try to imitate the output of a Newton-Raphson solver. It is robust to variations of injections, power grid topology, and line characteristics. We experimentally demonstrate the viability of our approach on standard IEEE power grids (case9, case14, case30 and case118) both in terms of accuracy and computational time." @default.
- W2991019969 created "2019-12-05" @default.
- W2991019969 creator A5006360335 @default.
- W2991019969 creator A5010808639 @default.
- W2991019969 creator A5023042687 @default.
- W2991019969 creator A5030058408 @default.
- W2991019969 creator A5060421461 @default.
- W2991019969 creator A5081938847 @default.
- W2991019969 date "2020-12-01" @default.
- W2991019969 modified "2023-10-09" @default.
- W2991019969 title "Neural networks for power flow: Graph neural solver" @default.
- W2991019969 cites W2059619246 @default.
- W2991019969 cites W2076063813 @default.
- W2991019969 cites W2106424475 @default.
- W2991019969 cites W2116341502 @default.
- W2991019969 cites W2165698076 @default.
- W2991019969 cites W2558748708 @default.
- W2991019969 cites W2593575282 @default.
- W2991019969 doi "https://doi.org/10.1016/j.epsr.2020.106547" @default.
- W2991019969 hasPublicationYear "2020" @default.
- W2991019969 type Work @default.
- W2991019969 sameAs 2991019969 @default.
- W2991019969 citedByCount "31" @default.
- W2991019969 countsByYear W29910199692020 @default.
- W2991019969 countsByYear W29910199692021 @default.
- W2991019969 countsByYear W29910199692022 @default.
- W2991019969 countsByYear W29910199692023 @default.
- W2991019969 crossrefType "journal-article" @default.
- W2991019969 hasAuthorship W2991019969A5006360335 @default.
- W2991019969 hasAuthorship W2991019969A5010808639 @default.
- W2991019969 hasAuthorship W2991019969A5023042687 @default.
- W2991019969 hasAuthorship W2991019969A5030058408 @default.
- W2991019969 hasAuthorship W2991019969A5060421461 @default.
- W2991019969 hasAuthorship W2991019969A5081938847 @default.
- W2991019969 hasBestOaLocation W29910199692 @default.
- W2991019969 hasConcept C121332964 @default.
- W2991019969 hasConcept C132525143 @default.
- W2991019969 hasConcept C154945302 @default.
- W2991019969 hasConcept C15744967 @default.
- W2991019969 hasConcept C163258240 @default.
- W2991019969 hasConcept C169760540 @default.
- W2991019969 hasConcept C199360897 @default.
- W2991019969 hasConcept C2778770139 @default.
- W2991019969 hasConcept C2986056383 @default.
- W2991019969 hasConcept C38349280 @default.
- W2991019969 hasConcept C41008148 @default.
- W2991019969 hasConcept C50644808 @default.
- W2991019969 hasConcept C57879066 @default.
- W2991019969 hasConcept C62520636 @default.
- W2991019969 hasConcept C80444323 @default.
- W2991019969 hasConcept C89227174 @default.
- W2991019969 hasConceptScore W2991019969C121332964 @default.
- W2991019969 hasConceptScore W2991019969C132525143 @default.
- W2991019969 hasConceptScore W2991019969C154945302 @default.
- W2991019969 hasConceptScore W2991019969C15744967 @default.
- W2991019969 hasConceptScore W2991019969C163258240 @default.
- W2991019969 hasConceptScore W2991019969C169760540 @default.
- W2991019969 hasConceptScore W2991019969C199360897 @default.
- W2991019969 hasConceptScore W2991019969C2778770139 @default.
- W2991019969 hasConceptScore W2991019969C2986056383 @default.
- W2991019969 hasConceptScore W2991019969C38349280 @default.
- W2991019969 hasConceptScore W2991019969C41008148 @default.
- W2991019969 hasConceptScore W2991019969C50644808 @default.
- W2991019969 hasConceptScore W2991019969C57879066 @default.
- W2991019969 hasConceptScore W2991019969C62520636 @default.
- W2991019969 hasConceptScore W2991019969C80444323 @default.
- W2991019969 hasConceptScore W2991019969C89227174 @default.
- W2991019969 hasLocation W29910199691 @default.
- W2991019969 hasLocation W29910199692 @default.
- W2991019969 hasLocation W29910199693 @default.
- W2991019969 hasLocation W29910199694 @default.
- W2991019969 hasLocation W29910199695 @default.
- W2991019969 hasOpenAccess W2991019969 @default.
- W2991019969 hasPrimaryLocation W29910199691 @default.
- W2991019969 hasRelatedWork W1553604823 @default.
- W2991019969 hasRelatedWork W1966025497 @default.
- W2991019969 hasRelatedWork W2149122936 @default.
- W2991019969 hasRelatedWork W2195994862 @default.
- W2991019969 hasRelatedWork W314331466 @default.
- W2991019969 hasRelatedWork W4206451355 @default.
- W2991019969 hasRelatedWork W4255427455 @default.
- W2991019969 hasRelatedWork W68941528 @default.
- W2991019969 hasRelatedWork W8322802 @default.
- W2991019969 hasRelatedWork W2186864281 @default.
- W2991019969 hasVolume "189" @default.
- W2991019969 isParatext "false" @default.
- W2991019969 isRetracted "false" @default.
- W2991019969 magId "2991019969" @default.
- W2991019969 workType "article" @default.