Residential Collegefalse
Status已發表Published
Dynamic response improvement for multi-terminal DC system with AI-designed adaptive dynamic reference control
Yang, Qifan; Lin, Gang; Jin, Xin; Zhang, Bin; Dai, Ningyi
2024-07-01
Source PublicationInternational Journal of Electrical Power and Energy Systems
ISSN0142-0615
Volume158Pages:109967
Abstract

The significant penetration of renewable energy sources (RES) makes the AC/DC hybrid system a typical low-inertia and poor-damping system. The multi-terminal direct current system (MTDC) is critical in connecting multiple AC systems and transferring bulk power among different AC regions. Conventional control often involves a tradeoff between response time and damping oscillations. To solve these issues, an AI-designed adaptive dynamic reference (ADR) control is investigated in this paper. It is designed with a controllable settling time and is able to mitigate the power oscillation during step change of power reference. In detail, an ADR module is utilized to generate the reference signal, which is input to dual closed-loop control. Raw data is generated from the simulation to build a data-driven surrogate model to map ADR parameters directly to settling time and overshoot in dynamic response. Once the surrogate model is trained, it is able to evaluate VSC's dynamic performance within 0.003s, which is much faster in comparison with online simulation. A meta-heuristic optimization method is then adopted to find the optimal control parameters based on the surrogate model. The effectiveness of the proposed ADR control and its AI-aided parameter design is validated by the real-time digital simulation and hardware-in-loop experiment.

KeywordAdaptive Dynamic Reference Dynamic Response Parameter Design Voltage Source Converter
DOI10.1016/j.ijepes.2024.109967
URLView the original
Indexed BySCIE
Language英語English
WOS Research AreaEngineering
WOS SubjectEngineering, Electrical & Electronic
WOS IDWOS:001228280400001
PublisherELSEVIER SCI LTD, THE BOULEVARD, LANGFORD LANE, KIDLINGTON, OXFORD OX5 1GB, OXON, ENGLAND
Scopus ID2-s2.0-85189630986
Fulltext Access
Citation statistics
Document TypeJournal article
CollectionTHE STATE KEY LABORATORY OF INTERNET OF THINGS FOR SMART CITY (UNIVERSITY OF MACAU)
DEPARTMENT OF ELECTRICAL AND COMPUTER ENGINEERING
Corresponding AuthorLin, Gang; Dai, Ningyi
AffiliationState Key Laboratory of Internet of Things for Smart City, University of Macau, China
First Author AffilicationUniversity of Macau
Corresponding Author AffilicationUniversity of Macau
Recommended Citation
GB/T 7714
Yang, Qifan,Lin, Gang,Jin, Xin,et al. Dynamic response improvement for multi-terminal DC system with AI-designed adaptive dynamic reference control[J]. International Journal of Electrical Power and Energy Systems, 2024, 158, 109967.
APA Yang, Qifan., Lin, Gang., Jin, Xin., Zhang, Bin., & Dai, Ningyi (2024). Dynamic response improvement for multi-terminal DC system with AI-designed adaptive dynamic reference control. International Journal of Electrical Power and Energy Systems, 158, 109967.
MLA Yang, Qifan,et al."Dynamic response improvement for multi-terminal DC system with AI-designed adaptive dynamic reference control".International Journal of Electrical Power and Energy Systems 158(2024):109967.
Files in This Item:
There are no files associated with this item.
Related Services
Recommend this item
Bookmark
Usage statistics
Export to Endnote
Google Scholar
Similar articles in Google Scholar
[Yang, Qifan]'s Articles
[Lin, Gang]'s Articles
[Jin, Xin]'s Articles
Baidu academic
Similar articles in Baidu academic
[Yang, Qifan]'s Articles
[Lin, Gang]'s Articles
[Jin, Xin]'s Articles
Bing Scholar
Similar articles in Bing Scholar
[Yang, Qifan]'s Articles
[Lin, Gang]'s Articles
[Jin, Xin]'s Articles
Terms of Use
No data!
Social Bookmark/Share
All comments (0)
No comment.
 

Items in the repository are protected by copyright, with all rights reserved, unless otherwise indicated.