Please use this identifier to cite or link to this item:
https://hdl.handle.net/20.500.14279/18088
DC Field | Value | Language |
---|---|---|
dc.contributor.author | Karagiannopoulos, Stavros | - |
dc.contributor.author | Aristidou, Petros | - |
dc.contributor.author | Hug, Gabriela | - |
dc.date.accessioned | 2020-03-16T18:26:34Z | - |
dc.date.available | 2020-03-16T18:26:34Z | - |
dc.date.issued | 2019-11-02 | - |
dc.identifier.citation | IEEE Transactions on Smart Grid, 2018, vol. 10, no. 6, pp.6461-6471 | en_US |
dc.identifier.issn | 19493053 | - |
dc.description.abstract | The optimal control of distribution networks often requires monitoring and communication infrastructure, either centralized or distributed. However, most of the current distribution systems lack this kind of infrastructure and rely on suboptimal, fit-and-forget, local controls to ensure the security of the network. In this paper, we propose a data-driven algorithm that uses historical data, advanced optimization techniques, and machine learning methods, to design local controls that emulate the optimal behavior without the use of any communication. We demonstrate the performance of the optimized local control on a three-phase, unbalanced, low-voltage, distribution network. The results show that our data-driven methodology clearly outperforms standard industry local control and successfully imitates an optimal-power-flow-based control. | en_US |
dc.format | en_US | |
dc.language.iso | en | en_US |
dc.relation.ispartof | IEEE Transactions on Smart Grid | en_US |
dc.rights | © IEEE | en_US |
dc.subject | Active distribution networks | en_US |
dc.subject | Backward forward sweep power flow | en_US |
dc.subject | Data-driven control design | en_US |
dc.subject | decentralized control | en_US |
dc.subject | distributed energy resources | en_US |
dc.subject | machine learning | en_US |
dc.subject | OPF | en_US |
dc.title | Data-Driven Local Control Design for Active Distribution Grids Using Off-Line Optimal Power Flow and Machine Learning Techniques | en_US |
dc.type | Article | en_US |
dc.collaboration | Leeds University | en_US |
dc.collaboration | ETH Zurich | en_US |
dc.collaboration | Cyprus University of Technology | en_US |
dc.subject.category | Electrical Engineering - Electronic Engineering - Information Engineering | en_US |
dc.journals | Open Access | en_US |
dc.country | United Kingdom | en_US |
dc.country | Switzerland | en_US |
dc.country | Cyprus | en_US |
dc.subject.field | Engineering and Technology | en_US |
dc.publication | Peer Reviewed | en_US |
dc.identifier.doi | 10.1109/TSG.2019.2905348 | en_US |
dc.identifier.scopus | 2-s2.0-85072346668 | - |
dc.identifier.url | http://arxiv.org/abs/1808.01009v2 | - |
dc.relation.issue | 6 | en_US |
dc.relation.volume | 10 | en_US |
cut.common.academicyear | 2018-2019 | en_US |
dc.identifier.spage | 6461 | en_US |
dc.identifier.epage | 6471 | en_US |
item.fulltext | With Fulltext | - |
item.languageiso639-1 | en | - |
item.grantfulltext | open | - |
item.openairecristype | http://purl.org/coar/resource_type/c_6501 | - |
item.cerifentitytype | Publications | - |
item.openairetype | article | - |
crisitem.journal.journalissn | 1949-3053 | - |
crisitem.journal.publisher | IEEE | - |
crisitem.author.dept | Department of Electrical Engineering, Computer Engineering and Informatics | - |
crisitem.author.faculty | Faculty of Engineering and Technology | - |
crisitem.author.orcid | 0000-0003-4429-0225 | - |
crisitem.author.parentorg | Faculty of Engineering and Technology | - |
Appears in Collections: | Άρθρα/Articles |
Files in This Item:
File | Description | Size | Format | |
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manuscript.pdf | 874.81 kB | Adobe PDF | View/Open |
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