Please use this identifier to cite or link to this item: https://hdl.handle.net/20.500.14279/24626
DC FieldValueLanguage
dc.contributor.authorStanojev, Ognjen-
dc.contributor.authorKundacina, Ognjen-
dc.contributor.authorMarkovic, Uros-
dc.contributor.authorVrettos, Evangelos-
dc.contributor.authorAristidou, Petros-
dc.contributor.authorHug, Gabriela-
dc.date.accessioned2022-02-22T12:10:38Z-
dc.date.available2022-02-22T12:10:38Z-
dc.date.issued2021-04-
dc.identifier.citation52nd North American Power Symposium, NAPS, 2021, 11-13 April, Tempe, AZ, USAen_US
dc.identifier.isbn9781728181929-
dc.identifier.urihttps://hdl.handle.net/20.500.14279/24626-
dc.description.abstractThe 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.formatpdfen_US
dc.language.isoenen_US
dc.rights© IEEEen_US
dc.rights.urihttp://creativecommons.org/licenses/by-nc-nd/4.0/*
dc.subjectFrequency controlen_US
dc.subjectLow-inertia systemsen_US
dc.subjectReinforcement learningen_US
dc.subjectVoltage source converteren_US
dc.titleA Reinforcement Learning Approach for Fast Frequency Control in Low-Inertia Power Systemsen_US
dc.typeConference Papersen_US
dc.collaborationUniversity of Zurichen_US
dc.collaborationUniversity of Novi Saden_US
dc.collaborationSwissgrid AGen_US
dc.collaborationCyprus University of Technologyen_US
dc.subject.categoryMechanical Engineeringen_US
dc.journalsSubscriptionen_US
dc.countrySwitzerlanden_US
dc.countrySerbiaen_US
dc.countryCyprusen_US
dc.subject.fieldEngineering and Technologyen_US
dc.publicationPeer Revieweden_US
dc.relation.conferenceNorth American Power Symposiumen_US
dc.identifier.doi10.1109/NAPS50074.2021.9449821en_US
dc.identifier.scopus2-s2.0-85113360234-
dc.identifier.urlhttps://api.elsevier.com/content/abstract/scopus_id/85113360234-
cut.common.academicyear2020-2021en_US
item.openairetypeconferenceObject-
item.cerifentitytypePublications-
item.fulltextNo Fulltext-
item.grantfulltextnone-
item.openairecristypehttp://purl.org/coar/resource_type/c_c94f-
item.languageiso639-1en-
crisitem.author.deptDepartment of Electrical Engineering, Computer Engineering and Informatics-
crisitem.author.facultyFaculty of Engineering and Technology-
crisitem.author.orcid0000-0003-4429-0225-
crisitem.author.parentorgFaculty of Engineering and Technology-
Appears in Collections:Δημοσιεύσεις σε συνέδρια /Conference papers or poster or presentation
CORE Recommender
Show simple item record

SCOPUSTM   
Citations 50

6
checked on Mar 14, 2024

Page view(s) 50

240
Last Week
0
Last month
1
checked on Jan 30, 2025

Google ScholarTM

Check

Altmetric


This item is licensed under a Creative Commons License Creative Commons