dc.contributor.author | Bana, Prabhat Ranjan | |
dc.contributor.author | Amin, Mohammad | |
dc.date.accessioned | 2023-04-18T06:47:04Z | |
dc.date.available | 2023-04-18T06:47:04Z | |
dc.date.created | 2023-04-17T16:50:04Z | |
dc.date.issued | 2023 | |
dc.identifier.citation | IEEE Journal of Emerging and Selected Topics in Industrial Electronics. 2023, . | en_US |
dc.identifier.issn | 2687-9735 | |
dc.identifier.uri | https://hdl.handle.net/11250/3063449 | |
dc.description.abstract | The rapid penetration of renewable energy sources into the power system makes the grid-connected voltage source converter (VSC) highly dynamic and uncertain. This necessitates designing new adaptive control for VSCs to ensure satisfactory system performance, reliability, and stability. This paper introduces a physics-informed artificial neural network (ANN) controller for the grid-connected VSC to improve the system performance and dampen the voltage oscillation due to the sudden change in power demand. The employed ANN structure is a feed-forward multilayer Neural Network trained offline by the Levenberg-Marquardt-based backpropagation algorithm. Results are presented for different dynamic scenarios to show the satisfactory operation of the proposed controller. The small-signal stability analysis is presented to validate the system's stability. Further, the performance of the proposed ANN controller is compared with the widely-used PI-controller and model predictive controller. The results prove that the proposed controller has a better dynamic performance in damping the voltage oscillation. | en_US |
dc.description.abstract | Control for Grid-Connected VSC With Improved Damping Based on Physics-Informed Neural Network | en_US |
dc.language.iso | eng | en_US |
dc.publisher | IEEE | en_US |
dc.title | Control for Grid-Connected VSC With Improved Damping Based on Physics-Informed Neural Network | en_US |
dc.title.alternative | Control for Grid-Connected VSC With Improved Damping Based on Physics-Informed Neural Network | en_US |
dc.type | Peer reviewed | en_US |
dc.type | Journal article | en_US |
dc.description.version | publishedVersion | en_US |
dc.rights.holder | © Copyright 2023 IEEE | en_US |
dc.source.pagenumber | 11 | en_US |
dc.source.journal | IEEE Journal of Emerging and Selected Topics in Industrial Electronics | en_US |
dc.identifier.doi | 10.1109/JESTIE.2023.3258339 | |
dc.identifier.cristin | 2141373 | |
dc.relation.project | Norges teknisk-naturvitenskapelige universitet: 81770920 | en_US |
cristin.ispublished | true | |
cristin.fulltext | original | |
cristin.qualitycode | 1 | |