{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,9,23]],"date-time":"2026-09-23T21:27:13Z","timestamp":1790198833421,"version":"4.1.0"},"reference-count":31,"publisher":"Wiley","license":[{"start":{"date-parts":[[2022,4,7]],"date-time":"2022-04-07T00:00:00Z","timestamp":1649289600000},"content-version":"unspecified","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/501100003416","name":"German Jordanian University","doi-asserted-by":"crossref","award":["SATS 03\/2020"],"award-info":[{"award-number":["SATS 03\/2020"]}],"id":[{"id":"10.13039\/501100003416","id-type":"DOI","asserted-by":"crossref"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Computational Intelligence and Neuroscience"],"published-print":{"date-parts":[[2022,4,7]]},"abstract":"<jats:p>Evaluating the resiliency of power systems against abnormal operational conditions is crucial for adapting effective actions in planning and operation. This paper introduces the level-of-resilience (LoR) measure to assess power system resiliency in terms of the minimum number of faults needed to produce a system outage (blackout) under sequential topology attacks. Four deep reinforcement learning (DRL)-based agents: deep Q-network (DQN), double DQN, the REINFORCE (Monte-Carlo policy gradient), and REINFORCE with baseline are used to determine the LoR. In this paper, three case studies based on IEEE 6-bus test system are investigated. The results demonstrate that the double DQN network agent achieved the highest success rate, and it was the fastest among the other agents. Thus, it can be an efficient agent for resiliency evaluation.<\/jats:p>","DOI":"10.1155\/2022\/2017366","type":"journal-article","created":{"date-parts":[[2022,4,7]],"date-time":"2022-04-07T18:50:05Z","timestamp":1649357405000},"page":"1-10","source":"Crossref","is-referenced-by-count":17,"title":["Resiliency Assessment of Power Systems Using Deep Reinforcement Learning"],"prefix":"10.1155","volume":"2022","author":[{"ORCID":"https:\/\/orcid.org\/0000-0001-5683-3317","authenticated-orcid":true,"given":"Mariam","family":"Ibrahim","sequence":"first","affiliation":[{"name":"Department of Mechatronics Engineering, German Jordanian University, Amman 11180, Jordan"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-3940-8519","authenticated-orcid":true,"given":"Ahmad","family":"Alsheikh","sequence":"additional","affiliation":[{"name":"Department of Mechatronics Engineering, German Jordanian University, Amman 11180, Jordan"},{"name":"Department of Natural Science & Industrial Engineering, Deggendorf Institute of Technology, Deggendorf 94469, Germany"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-8550-5061","authenticated-orcid":true,"given":"Ruba","family":"Elhafiz","sequence":"additional","affiliation":[{"name":"Department of Mechatronics Engineering, German Jordanian University, Amman 11180, Jordan"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"311","reference":[{"key":"1","doi-asserted-by":"publisher","DOI":"10.1061\/(asce)is.1943-555x.0000477"},{"key":"2","first-page":"2634","article-title":"Power grid vulnerability to geographically correlated failures\u2014analysis and control implications","author":"A. 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