{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2022,4,5]],"date-time":"2022-04-05T16:27:38Z","timestamp":1649176058732},"reference-count":0,"publisher":"IOS Press","license":[{"start":{"date-parts":[[2021,12,22]],"date-time":"2021-12-22T00:00:00Z","timestamp":1640131200000},"content-version":"unspecified","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by-nc\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2021,12,22]]},"abstract":"<jats:p>With the gradual reform and development of the power grid, it is of great significance to study how to effectively identify and evaluate the weak links of the power grid for the actual planning, construction, and operation of the power grid. This paper analyzed the power grid\u2019s historical component data and real-time operation state parameters. We established a weak link identification model based on Bayesian reasoning. Firstly, we constructed the node branch Bayesian network according to the network topology relationship. The power transmission distribution factor is modified according to the historical operation load of the grid components, and the conditional probability table is calculated based on the grid structure; finally, we used the maximum possible explanation algorithm in the Bayesian network. The weakness degree of all components in the network is calculated, and the maximum probability weak link sequence is obtained. The correctness and effectiveness of the proposed method are verified by IEEE 39 bus simulation and regional power grid data.<\/jats:p>","DOI":"10.3233\/faia210400","type":"book-chapter","created":{"date-parts":[[2021,12,29]],"date-time":"2021-12-29T10:25:49Z","timestamp":1640773549000},"source":"Crossref","is-referenced-by-count":0,"title":["Research on Identification of Power Grid Weakness Based on Bayesian Inference"],"prefix":"10.3233","author":[{"given":"Jiang","family":"Hu","sequence":"first","affiliation":[{"name":"Grid Planning & Research Center, Guizhou Power Grid Co., Ltd., Guiyang, Guizhou, 550003, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Wei","family":"Li","sequence":"additional","affiliation":[{"name":"Guiyang Power Supply Bureau, Guizhou Power Grid Co., Ltd., Guiyang, Guizhou, 550003, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Wenxia","family":"Liu","sequence":"additional","affiliation":[{"name":"Grid Planning & Research Center, Guizhou Power Grid Co., Ltd., Guiyang, Guizhou, 550003, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Xianggang","family":"He","sequence":"additional","affiliation":[{"name":"Grid Planning & Research Center, Guizhou Power Grid Co., Ltd., Guiyang, Guizhou, 550003, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Yu","family":"Zhang","sequence":"additional","affiliation":[{"name":"Grid Planning & Research Center, Guizhou Power Grid Co., Ltd., Guiyang, Guizhou, 550003, China"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"7437","container-title":["Frontiers in Artificial Intelligence and Applications","Proceedings of CECNet 2021"],"original-title":[],"link":[{"URL":"https:\/\/ebooks.iospress.nl\/pdf\/doi\/10.3233\/FAIA210400","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2021,12,29]],"date-time":"2021-12-29T10:25:50Z","timestamp":1640773550000},"score":1,"resource":{"primary":{"URL":"https:\/\/ebooks.iospress.nl\/doi\/10.3233\/FAIA210400"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2021,12,22]]},"references-count":0,"URL":"https:\/\/doi.org\/10.3233\/faia210400","relation":{},"ISSN":["0922-6389","1879-8314"],"issn-type":[{"value":"0922-6389","type":"print"},{"value":"1879-8314","type":"electronic"}],"subject":[],"published":{"date-parts":[[2021,12,22]]}}}