Matches in SemOpenAlex for { <https://semopenalex.org/work/W4312922872> ?p ?o ?g. }
Showing items 1 to 78 of
78
with 100 items per page.
- W4312922872 abstract "Fast and accurate conversion loss models are becoming crucial for the reliable and efficient operation of hybrid AC/DC microgrids (MGs). However, the traditionally applied conversion loss surrogate modeling methods are either too simple to capture the nonlinearity of the conversion losses, or too complex to fit in MG energy management problems. In this study, a neural network-based linear surrogate modeling method is developed to provide fast and accurate approximations of conversion losses. We first generate the training and test data using PLECS simulation models. Then, the neural network is trained using the augmented Lagrangian method to enforce additional hard constraints to the conversion loss-related variables. Once trained, the proposed neural network model is reformulated into a mixed-integer linear programming (MILP) model, which can be subsequently used in MG energy management problems. We compare the proposed model against other commonly used linear surrogate models on the test data to examine the model performance. The experiment results indicate that the proposed model yields significantly smaller relative error than other commonly used linear surrogate models and can be solved efficiently using state-of-the-art MILP solvers." @default.
- W4312922872 created "2023-01-05" @default.
- W4312922872 creator A5007551611 @default.
- W4312922872 creator A5009407777 @default.
- W4312922872 creator A5027937292 @default.
- W4312922872 creator A5038061233 @default.
- W4312922872 date "2022-07-08" @default.
- W4312922872 modified "2023-09-27" @default.
- W4312922872 title "Accurate and Efficient Linear Surrogate Models for Conversion Losses in Hybrid AC/DC Microgrids based on Hard Constrained Neural Networks" @default.
- W4312922872 cites W2001445580 @default.
- W4312922872 cites W2137983211 @default.
- W4312922872 cites W2164278890 @default.
- W4312922872 cites W2167556886 @default.
- W4312922872 cites W2676889042 @default.
- W4312922872 cites W2743413514 @default.
- W4312922872 cites W2997656918 @default.
- W4312922872 cites W3014808184 @default.
- W4312922872 doi "https://doi.org/10.1109/icpsasia55496.2022.9949964" @default.
- W4312922872 hasPublicationYear "2022" @default.
- W4312922872 type Work @default.
- W4312922872 citedByCount "0" @default.
- W4312922872 crossrefType "proceedings-article" @default.
- W4312922872 hasAuthorship W4312922872A5007551611 @default.
- W4312922872 hasAuthorship W4312922872A5009407777 @default.
- W4312922872 hasAuthorship W4312922872A5027937292 @default.
- W4312922872 hasAuthorship W4312922872A5038061233 @default.
- W4312922872 hasConcept C105795698 @default.
- W4312922872 hasConcept C11413529 @default.
- W4312922872 hasConcept C119857082 @default.
- W4312922872 hasConcept C121332964 @default.
- W4312922872 hasConcept C126255220 @default.
- W4312922872 hasConcept C131675550 @default.
- W4312922872 hasConcept C154945302 @default.
- W4312922872 hasConcept C158622935 @default.
- W4312922872 hasConcept C163175372 @default.
- W4312922872 hasConcept C186370098 @default.
- W4312922872 hasConcept C199360897 @default.
- W4312922872 hasConcept C33923547 @default.
- W4312922872 hasConcept C41008148 @default.
- W4312922872 hasConcept C41045048 @default.
- W4312922872 hasConcept C50644808 @default.
- W4312922872 hasConcept C56086750 @default.
- W4312922872 hasConcept C62520636 @default.
- W4312922872 hasConcept C97137487 @default.
- W4312922872 hasConceptScore W4312922872C105795698 @default.
- W4312922872 hasConceptScore W4312922872C11413529 @default.
- W4312922872 hasConceptScore W4312922872C119857082 @default.
- W4312922872 hasConceptScore W4312922872C121332964 @default.
- W4312922872 hasConceptScore W4312922872C126255220 @default.
- W4312922872 hasConceptScore W4312922872C131675550 @default.
- W4312922872 hasConceptScore W4312922872C154945302 @default.
- W4312922872 hasConceptScore W4312922872C158622935 @default.
- W4312922872 hasConceptScore W4312922872C163175372 @default.
- W4312922872 hasConceptScore W4312922872C186370098 @default.
- W4312922872 hasConceptScore W4312922872C199360897 @default.
- W4312922872 hasConceptScore W4312922872C33923547 @default.
- W4312922872 hasConceptScore W4312922872C41008148 @default.
- W4312922872 hasConceptScore W4312922872C41045048 @default.
- W4312922872 hasConceptScore W4312922872C50644808 @default.
- W4312922872 hasConceptScore W4312922872C56086750 @default.
- W4312922872 hasConceptScore W4312922872C62520636 @default.
- W4312922872 hasConceptScore W4312922872C97137487 @default.
- W4312922872 hasLocation W43129228721 @default.
- W4312922872 hasOpenAccess W4312922872 @default.
- W4312922872 hasPrimaryLocation W43129228721 @default.
- W4312922872 hasRelatedWork W1991174016 @default.
- W4312922872 hasRelatedWork W2004060844 @default.
- W4312922872 hasRelatedWork W2038467420 @default.
- W4312922872 hasRelatedWork W2057820268 @default.
- W4312922872 hasRelatedWork W2080202770 @default.
- W4312922872 hasRelatedWork W2313267066 @default.
- W4312922872 hasRelatedWork W2478592842 @default.
- W4312922872 hasRelatedWork W2660246441 @default.
- W4312922872 hasRelatedWork W3795022 @default.
- W4312922872 hasRelatedWork W74652278 @default.
- W4312922872 isParatext "false" @default.
- W4312922872 isRetracted "false" @default.
- W4312922872 workType "article" @default.