Please use this identifier to cite or link to this item:
https://hdl.handle.net/20.500.14279/24626
DC Field | Value | Language |
---|---|---|
dc.contributor.author | Stanojev, Ognjen | - |
dc.contributor.author | Kundacina, Ognjen | - |
dc.contributor.author | Markovic, Uros | - |
dc.contributor.author | Vrettos, Evangelos | - |
dc.contributor.author | Aristidou, Petros | - |
dc.contributor.author | Hug, Gabriela | - |
dc.date.accessioned | 2022-02-22T12:10:38Z | - |
dc.date.available | 2022-02-22T12:10:38Z | - |
dc.date.issued | 2021-04 | - |
dc.identifier.citation | 52nd North American Power Symposium, NAPS, 2021, 11-13 April, Tempe, AZ, USA | en_US |
dc.identifier.isbn | 9781728181929 | - |
dc.identifier.uri | https://hdl.handle.net/20.500.14279/24626 | - |
dc.description.abstract | The electric grid is undergoing a major transition from fossil fuel-based power generation to renewable energy sources, typically interfaced to the grid via power electronics. The future power systems are thus expected to face increased control complexity and challenges pertaining to frequency stability due to lower levels of inertia and damping. As a result, the frequency control and development of novel ancillary services is becoming imperative. This paper proposes a data-driven control scheme, based on Reinforcement Learning (RL), for grid-forming Voltage Source Converters (VSCs), with the goal of exploiting their fast response capabilities to provide fast frequency control to the system. A centralized RL-based controller collects generator frequencies and adjusts the VSC power output, in response to a disturbance, to prevent frequency threshold violations. The proposed control scheme is analyzed and its performance evaluated through detailed time-domain simulations of the IEEE 14-bus test system. | en_US |
dc.format | en_US | |
dc.language.iso | en | en_US |
dc.rights | © IEEE | en_US |
dc.rights.uri | http://creativecommons.org/licenses/by-nc-nd/4.0/ | * |
dc.subject | Frequency control | en_US |
dc.subject | Low-inertia systems | en_US |
dc.subject | Reinforcement learning | en_US |
dc.subject | Voltage source converter | en_US |
dc.title | A Reinforcement Learning Approach for Fast Frequency Control in Low-Inertia Power Systems | en_US |
dc.type | Conference Papers | en_US |
dc.collaboration | University of Zurich | en_US |
dc.collaboration | University of Novi Sad | en_US |
dc.collaboration | Swissgrid AG | en_US |
dc.collaboration | Cyprus University of Technology | en_US |
dc.subject.category | Mechanical Engineering | en_US |
dc.journals | Subscription | en_US |
dc.country | Switzerland | en_US |
dc.country | Serbia | en_US |
dc.country | Cyprus | en_US |
dc.subject.field | Engineering and Technology | en_US |
dc.publication | Peer Reviewed | en_US |
dc.relation.conference | North American Power Symposium | en_US |
dc.identifier.doi | 10.1109/NAPS50074.2021.9449821 | en_US |
dc.identifier.scopus | 2-s2.0-85113360234 | - |
dc.identifier.url | https://api.elsevier.com/content/abstract/scopus_id/85113360234 | - |
cut.common.academicyear | 2020-2021 | en_US |
item.openairetype | conferenceObject | - |
item.cerifentitytype | Publications | - |
item.fulltext | No Fulltext | - |
item.grantfulltext | none | - |
item.openairecristype | http://purl.org/coar/resource_type/c_c94f | - |
item.languageiso639-1 | en | - |
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: | Δημοσιεύσεις σε συνέδρια /Conference papers or poster or presentation |
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