{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,2,21]],"date-time":"2025-02-21T17:33:49Z","timestamp":1740159229902,"version":"3.37.3"},"reference-count":17,"publisher":"World Scientific Pub Co Pte Ltd","issue":"01","content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Int. J. Model. Simul. Sci. Comput."],"published-print":{"date-parts":[[2018,2]]},"abstract":"<jats:p> Power grid partitioning decomposes a large power grid into several clusters. Most of the existing partitioning methods suffer from a limitation that the buses within a cluster are severely topologically disconnected after partitioning in some cases. As a result, a cluster will inevitably be assigned to two or more power grid corporations. This assignment obstructs inner-cluster monitoring and control applications of the transmission system. To overcome the limitation, this paper proposes a multi-index power grid partitioning approach using Monte Carlo simulation guaranteeing cluster connectivity to ensure the cluster autonomy. A line-based binary coding technique is developed to ensure the cluster connectivity. Three partitioning indices are considered: the coherency, the cluster connectivity, and the number of clusters. Finally, the proposed partitioning method is applied to IEEE 9-bus system, IEEE 39-bus system and IEEE 145-bus system and compared with Fuzzy C-medoid (FCMdd) algorithm. <\/jats:p>","DOI":"10.1142\/s1793962317500532","type":"journal-article","created":{"date-parts":[[2017,5,8]],"date-time":"2017-05-08T02:14:38Z","timestamp":1494209678000},"page":"1750053","source":"Crossref","is-referenced-by-count":3,"title":["A cluster-autonomous partitioning algorithm in electrical power grid using Monte Carlo simulation"],"prefix":"10.1142","volume":"09","author":[{"ORCID":"https:\/\/orcid.org\/0000-0001-7220-4756","authenticated-orcid":false,"given":"Chengchao","family":"Lu","sequence":"first","affiliation":[{"name":"Department of Control Science and Engineering, Tongji University, Shanghai, 201804, P. R. China"}]},{"given":"Zhongjie","family":"Wang","sequence":"additional","affiliation":[{"name":"Department of Control Science and Engineering, Tongji University, Shanghai, 201804, P. R. 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