{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,6,21]],"date-time":"2026-06-21T07:16:27Z","timestamp":1782026187596,"version":"3.54.5"},"reference-count":30,"publisher":"Institute of Electrical and Electronics Engineers (IEEE)","issue":"3","license":[{"start":{"date-parts":[[2022,3,1]],"date-time":"2022-03-01T00:00:00Z","timestamp":1646092800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/ieeexplore.ieee.org\/Xplorehelp\/downloads\/license-information\/IEEE.html"},{"start":{"date-parts":[[2022,3,1]],"date-time":"2022-03-01T00:00:00Z","timestamp":1646092800000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-029"},{"start":{"date-parts":[[2022,3,1]],"date-time":"2022-03-01T00:00:00Z","timestamp":1646092800000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-037"}],"funder":[{"DOI":"10.13039\/501100000266","name":"Engineering and Physical Sciences Research Council","doi-asserted-by":"publisher","award":["EP\/S001905\/1"],"award-info":[{"award-number":["EP\/S001905\/1"]}],"id":[{"id":"10.13039\/501100000266","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["IEEE Trans. Ind. Inf."],"published-print":{"date-parts":[[2022,3]]},"DOI":"10.1109\/tii.2021.3093300","type":"journal-article","created":{"date-parts":[[2021,7,1]],"date-time":"2021-07-01T20:05:17Z","timestamp":1625169917000},"page":"1706-1715","source":"Crossref","is-referenced-by-count":44,"title":["Wind-Farm Power Tracking Via Preview-Based Robust Reinforcement Learning"],"prefix":"10.1109","volume":"18","author":[{"ORCID":"https:\/\/orcid.org\/0000-0003-4302-5323","authenticated-orcid":false,"given":"Hongyang","family":"Dong","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-1182-4502","authenticated-orcid":false,"given":"Xiaowei","family":"Zhao","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"263","reference":[{"key":"ref30","doi-asserted-by":"publisher","DOI":"10.5194\/wes-5-225-2020"},{"key":"ref10","doi-asserted-by":"publisher","DOI":"10.1109\/ACC.2016.7525115"},{"key":"ref11","article-title":"Controlling wind turbines for secondary frequency regulation: An analysis of AGC capabilities under new performance based compensation policy","author":"aho","year":"2015"},{"key":"ref12","doi-asserted-by":"publisher","DOI":"10.1109\/TSTE.2015.2418282"},{"key":"ref13","doi-asserted-by":"publisher","DOI":"10.1016\/j.ifacol.2017.08.378"},{"key":"ref14","doi-asserted-by":"publisher","DOI":"10.23919\/ACC.2018.8431764"},{"key":"ref15","doi-asserted-by":"publisher","DOI":"10.23919\/ACC.2019.8814475"},{"key":"ref16","doi-asserted-by":"publisher","DOI":"10.1016\/j.renene.2018.11.031"},{"key":"ref17","author":"sutton","year":"2018","journal-title":"Reinforcement Learning An Introduction"},{"key":"ref18","doi-asserted-by":"publisher","DOI":"10.1109\/TII.2020.3017573"},{"key":"ref19","doi-asserted-by":"publisher","DOI":"10.1109\/TCST.2020.3007401"},{"key":"ref28","doi-asserted-by":"publisher","DOI":"10.1038\/nature14236"},{"key":"ref4","doi-asserted-by":"publisher","DOI":"10.1016\/j.renene.2017.06.065"},{"key":"ref27","doi-asserted-by":"publisher","DOI":"10.1109\/TSMC.2018.2863708"},{"key":"ref3","doi-asserted-by":"publisher","DOI":"10.1109\/TCST.2015.2508007"},{"key":"ref6","article-title":"Artificial neural network based reinforcement learning for wind turbine yaw control","volume":"12","author":"saenz-aguirre","year":"2019","journal-title":"Energy ”"},{"key":"ref29","doi-asserted-by":"publisher","DOI":"10.5194\/wes-3-75-2018"},{"key":"ref5","doi-asserted-by":"publisher","DOI":"10.23919\/ACC45564.2020.9147946"},{"key":"ref8","doi-asserted-by":"publisher","DOI":"10.1016\/j.apenergy.2021.116928"},{"key":"ref7","doi-asserted-by":"publisher","DOI":"10.1109\/TII.2020.2974037"},{"key":"ref2","doi-asserted-by":"publisher","DOI":"10.1016\/j.energy.2020.118415"},{"key":"ref9","doi-asserted-by":"publisher","DOI":"10.1002\/we.2093"},{"key":"ref1","doi-asserted-by":"publisher","DOI":"10.5194\/wes-5-309-2020"},{"key":"ref20","doi-asserted-by":"publisher","DOI":"10.1109\/TII.2020.3001095"},{"key":"ref22","article-title":"Continuous control with deep reinforcement learning","author":"lillicrap","year":"2016","journal-title":"Proc 4th Int Conf Learn Representations"},{"key":"ref21","doi-asserted-by":"crossref","first-page":"354","DOI":"10.1038\/nature24270","article-title":"Mastering the game of go without human knowledge","volume":"550","author":"silver","year":"2017","journal-title":"Nature"},{"key":"ref24","doi-asserted-by":"publisher","DOI":"10.1109\/TCYB.2020.2970969"},{"key":"ref23","first-page":"2040","article-title":"Robust deep reinforcement learning with adversarial attacks","author":"pattanaik","year":"2018","journal-title":"Proc 17th Int Conf Auton Agents MultiAgent Syst"},{"key":"ref26","doi-asserted-by":"publisher","DOI":"10.1109\/TCYB.2016.2643687"},{"key":"ref25","doi-asserted-by":"publisher","DOI":"10.1109\/TNNLS.2016.2585520"}],"container-title":["IEEE Transactions on Industrial Informatics"],"original-title":[],"link":[{"URL":"http:\/\/xplorestaging.ieee.org\/ielx7\/9424\/9640533\/09470926.pdf?arnumber=9470926","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2022,4,8]],"date-time":"2022-04-08T17:59:30Z","timestamp":1649440770000},"score":1,"resource":{"primary":{"URL":"https:\/\/ieeexplore.ieee.org\/document\/9470926\/"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2022,3]]},"references-count":30,"journal-issue":{"issue":"3"},"URL":"https:\/\/doi.org\/10.1109\/tii.2021.3093300","relation":{},"ISSN":["1551-3203","1941-0050"],"issn-type":[{"value":"1551-3203","type":"print"},{"value":"1941-0050","type":"electronic"}],"subject":[],"published":{"date-parts":[[2022,3]]}}}