{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,10,1]],"date-time":"2026-10-01T09:36:57Z","timestamp":1790847417941,"version":"4.1.0"},"reference-count":40,"publisher":"Institute of Electrical and Electronics Engineers (IEEE)","issue":"2","license":[{"start":{"date-parts":[[2023,2,1]],"date-time":"2023-02-01T00:00:00Z","timestamp":1675209600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/ieeexplore.ieee.org\/Xplorehelp\/downloads\/license-information\/IEEE.html"},{"start":{"date-parts":[[2023,2,1]],"date-time":"2023-02-01T00:00:00Z","timestamp":1675209600000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-029"},{"start":{"date-parts":[[2023,2,1]],"date-time":"2023-02-01T00:00:00Z","timestamp":1675209600000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-037"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["IEEE Trans. Ind. Inf."],"published-print":{"date-parts":[[2023,2]]},"DOI":"10.1109\/tii.2022.3182328","type":"journal-article","created":{"date-parts":[[2022,6,13]],"date-time":"2022-06-13T16:54:06Z","timestamp":1655139246000},"page":"1725-1734","source":"Crossref","is-referenced-by-count":41,"title":["A Multiagent Reinforcement Learning Approach for Wind Farm Frequency Control"],"prefix":"10.1109","volume":"19","author":[{"ORCID":"https:\/\/orcid.org\/0000-0003-3216-9583","authenticated-orcid":false,"given":"Yanchang","family":"Liang","sequence":"first","affiliation":[{"name":"Intelligent Control and Smart Energy Research Group, School of Engineering, University of Warwick, Coventry, U.K."}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-1182-4502","authenticated-orcid":false,"given":"Xiaowei","family":"Zhao","sequence":"additional","affiliation":[{"name":"Intelligent Control and Smart Energy Research Group, School of Engineering, University of Warwick, Coventry, U.K."}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-8866-7875","authenticated-orcid":false,"given":"Li","family":"Sun","sequence":"additional","affiliation":[{"name":"School of Mechanical Engineering and Automation, Harbin Institute of Technology, Shenzhen, China"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"263","reference":[{"key":"ref39","doi-asserted-by":"publisher","DOI":"10.2514\/6.2000-50"},{"key":"ref38","doi-asserted-by":"publisher","DOI":"10.1016\/0142-1123(84)90032-X"},{"key":"ref33","doi-asserted-by":"publisher","DOI":"10.1109\/ACC.2012.6315180"},{"key":"ref32","doi-asserted-by":"crossref","DOI":"10.1109\/TPWRS.2005.861952","article-title":"Power system stability","author":"kundur","year":"2006","journal-title":"Power System Stability and Control"},{"key":"ref31","article-title":"High-dimensional continuous control using generalized advantage estimation","author":"schulman","year":"2015"},{"key":"ref30","doi-asserted-by":"publisher","DOI":"10.1512\/iumj.1957.6.56038"},{"key":"ref37","article-title":"The statistical variation of wind turbine fatigue loads","author":"thomsen","year":"1998"},{"key":"ref36","first-page":"6379","article-title":"Multi-agent actor-critic for mixed cooperative-competitive environments","author":"lowe","year":"0","journal-title":"Proc Adv Neural Inf Process Syst 30 Annu Conf Neural Inf Process Syst"},{"key":"ref35","first-page":"315","article-title":"Deep sparse rectifier neural networks","author":"glorot","year":"0","journal-title":"Proc 14th Int Conf Artif Intell Statist"},{"key":"ref34","doi-asserted-by":"publisher","DOI":"10.1080\/22348972.2015.1109793"},{"key":"ref10","doi-asserted-by":"publisher","DOI":"10.1049\/iet-gtd.2016.1245"},{"key":"ref40","doi-asserted-by":"publisher","DOI":"10.1088\/1742-6596\/75\/1\/012061"},{"key":"ref11","doi-asserted-by":"publisher","DOI":"10.1109\/JSYST.2016.2563979"},{"key":"ref12","doi-asserted-by":"publisher","DOI":"10.1109\/ICONSTEM.2017.8261385"},{"key":"ref13","doi-asserted-by":"publisher","DOI":"10.1109\/TPWRS.2012.2208233"},{"key":"ref14","doi-asserted-by":"publisher","DOI":"10.1109\/TSTE.2017.2690682"},{"key":"ref15","article-title":"OpenFAST documentation","year":"2022"},{"key":"ref16","doi-asserted-by":"crossref","first-page":"529","DOI":"10.1038\/nature14236","article-title":"Human-level control through deep reinforcement learning","volume":"518","author":"mnih","year":"2015","journal-title":"Nature"},{"key":"ref17","doi-asserted-by":"publisher","DOI":"10.1126\/science.aar6404"},{"key":"ref18","first-page":"4295","article-title":"QMIX: Monotonic value function factorisation for deep multi-agent reinforcement learning","author":"rashid","year":"0","journal-title":"Proc Int Conf Mach Learn"},{"key":"ref19","doi-asserted-by":"publisher","DOI":"10.1109\/TII.2020.2974037"},{"key":"ref28","article-title":"Slm lab: A comprehensive benchmark and modular software framework for reproducible deep reinforcement learning","author":"loon","year":"2019"},{"key":"ref4","doi-asserted-by":"publisher","DOI":"10.1109\/TSTE.2013.2280664"},{"key":"ref27","first-page":"1889","article-title":"Trust region policy optimization","author":"schulman","year":"0","journal-title":"Proc Int Conf Mach Learn"},{"key":"ref3","doi-asserted-by":"publisher","DOI":"10.1109\/TPWRS.2015.2417758"},{"key":"ref6","doi-asserted-by":"publisher","DOI":"10.1109\/TEC.2016.2532366"},{"key":"ref29","article-title":"Is independent learning all you need in the starcraft multi-agent challenge","author":"de witt","year":"2020"},{"key":"ref5","doi-asserted-by":"publisher","DOI":"10.1109\/TSTE.2015.2493165"},{"key":"ref8","doi-asserted-by":"publisher","DOI":"10.1109\/TIE.2016.2642882"},{"key":"ref7","doi-asserted-by":"publisher","DOI":"10.1109\/TIA.2018.2868644"},{"key":"ref2","doi-asserted-by":"publisher","DOI":"10.1109\/TPWRS.2008.920197"},{"key":"ref9","doi-asserted-by":"publisher","DOI":"10.1049\/iet-rpg.2018.5392"},{"key":"ref1","doi-asserted-by":"publisher","DOI":"10.1109\/TPWRS.2005.861956"},{"key":"ref20","doi-asserted-by":"publisher","DOI":"10.1109\/TII.2021.3095563"},{"key":"ref22","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-319-28929-8"},{"key":"ref21","doi-asserted-by":"publisher","DOI":"10.1109\/TII.2021.3093300"},{"key":"ref24","article-title":"Benchmarking multi-agent deep reinforcement learning algorithms in cooperative tasks","author":"papoudakis","year":"0","journal-title":"Proc Neural Inf Process Syst Track Datasets Benchmarks 1"},{"key":"ref23","article-title":"The surprising effectiveness of MAPPO in cooperative, multi-agent games","author":"yu","year":"2021"},{"key":"ref26","article-title":"Proximal policy optimization algorithms","author":"schulman","year":"2017"},{"key":"ref25","doi-asserted-by":"publisher","DOI":"10.5194\/wes-5-1339-2020"}],"container-title":["IEEE Transactions on Industrial Informatics"],"original-title":[],"link":[{"URL":"http:\/\/xplorestaging.ieee.org\/ielx7\/9424\/9989328\/09795254.pdf?arnumber=9795254","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2023,1,16]],"date-time":"2023-01-16T14:16:34Z","timestamp":1673878594000},"score":1,"resource":{"primary":{"URL":"https:\/\/ieeexplore.ieee.org\/document\/9795254\/"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2023,2]]},"references-count":40,"journal-issue":{"issue":"2"},"URL":"https:\/\/doi.org\/10.1109\/tii.2022.3182328","relation":{},"ISSN":["1551-3203","1941-0050"],"issn-type":[{"value":"1551-3203","type":"print"},{"value":"1941-0050","type":"electronic"}],"subject":[],"published":{"date-parts":[[2023,2]]}}}