Matches in SemOpenAlex for { <https://semopenalex.org/work/W4387163712> ?p ?o ?g. }
Showing items 1 to 53 of
53
with 100 items per page.
- W4387163712 abstract "When a branch fault occurs in the power distribution network, what kind of algorithm can be used to safely, quickly and accurately eliminate the fault and restore the power distribution network to the previous state is an urgent problem to be solved. Due to the diversity and complexity of the power distribution network topology, as well as the sparseness and inaccessibility of fault data, the processing of power distribution network fault recovery will be greatly affected. In order to solve various problems in power distribution network fault recovery, by combining the characteristics of reinforcement learning, such as autonomous learning and running without training samples, and the good data fit of neural network, a power distribution network fault based on DQN (Deep Q-Network) is proposed. The algorithm has been restored and some improvements have been made to the experience replay and reward mechanism in the algorithm, and a significant improvement has been achieved. Through the simulation experiments of fault recovery of power distribution networks with different structures, the results show that the algorithm can effectively realize the recovery processing function of the faulty branch." @default.
- W4387163712 created "2023-09-30" @default.
- W4387163712 creator A5029505910 @default.
- W4387163712 creator A5045648220 @default.
- W4387163712 date "2023-04-01" @default.
- W4387163712 modified "2023-09-30" @default.
- W4387163712 title "Power Distribution Network Fault Recovery Based on Improved DQN Algorithm" @default.
- W4387163712 cites W2071827395 @default.
- W4387163712 cites W2896918606 @default.
- W4387163712 cites W3106828215 @default.
- W4387163712 doi "https://doi.org/10.1109/ictech58362.2023.00084" @default.
- W4387163712 hasPublicationYear "2023" @default.
- W4387163712 type Work @default.
- W4387163712 citedByCount "0" @default.
- W4387163712 crossrefType "proceedings-article" @default.
- W4387163712 hasAuthorship W4387163712A5029505910 @default.
- W4387163712 hasAuthorship W4387163712A5045648220 @default.
- W4387163712 hasConcept C11413529 @default.
- W4387163712 hasConcept C121332964 @default.
- W4387163712 hasConcept C127313418 @default.
- W4387163712 hasConcept C154945302 @default.
- W4387163712 hasConcept C163258240 @default.
- W4387163712 hasConcept C165205528 @default.
- W4387163712 hasConcept C175551986 @default.
- W4387163712 hasConcept C41008148 @default.
- W4387163712 hasConcept C50644808 @default.
- W4387163712 hasConcept C62520636 @default.
- W4387163712 hasConceptScore W4387163712C11413529 @default.
- W4387163712 hasConceptScore W4387163712C121332964 @default.
- W4387163712 hasConceptScore W4387163712C127313418 @default.
- W4387163712 hasConceptScore W4387163712C154945302 @default.
- W4387163712 hasConceptScore W4387163712C163258240 @default.
- W4387163712 hasConceptScore W4387163712C165205528 @default.
- W4387163712 hasConceptScore W4387163712C175551986 @default.
- W4387163712 hasConceptScore W4387163712C41008148 @default.
- W4387163712 hasConceptScore W4387163712C50644808 @default.
- W4387163712 hasConceptScore W4387163712C62520636 @default.
- W4387163712 hasLocation W43871637121 @default.
- W4387163712 hasOpenAccess W4387163712 @default.
- W4387163712 hasPrimaryLocation W43871637121 @default.
- W4387163712 hasRelatedWork W2356557657 @default.
- W4387163712 hasRelatedWork W2365567737 @default.
- W4387163712 hasRelatedWork W2372829958 @default.
- W4387163712 hasRelatedWork W2373962361 @default.
- W4387163712 hasRelatedWork W2376596949 @default.
- W4387163712 hasRelatedWork W2380313759 @default.
- W4387163712 hasRelatedWork W2386387936 @default.
- W4387163712 hasRelatedWork W2386767533 @default.
- W4387163712 hasRelatedWork W2392110728 @default.
- W4387163712 hasRelatedWork W2394139458 @default.
- W4387163712 isParatext "false" @default.
- W4387163712 isRetracted "false" @default.
- W4387163712 workType "article" @default.