Please use this identifier to cite or link to this item: https://gnanaganga.inflibnet.ac.in:8443/jspui/handle/123456789/14996
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dc.contributor.authorTheodorakatos, Nikolaos P-
dc.contributor.authorBabu, Rohit-
dc.contributor.authorMoschoudis, Angelos P-
dc.date.accessioned2024-03-30T10:11:02Z-
dc.date.available2024-03-30T10:11:02Z-
dc.date.issued2024-
dc.identifier.citationVol. 2701, No. 1en_US
dc.identifier.issn1742-6588-
dc.identifier.urihttps://doi.org/10.1088/1742-6596/2701/1/012013-
dc.identifier.urihttp://gnanaganga.inflibnet.ac.in:8080/jspui/handle/123456789/14996-
dc.description.abstractThe purpose of this paper is to introduce several optimization algorithms that can be used to address optimization models in the power network, where the level of observability may be either complete or incomplete. These algorithms include discrete, continuous and metaheuristic methods. Initially, the optimization problem is approached by implementing a zero-one mixed integer linear program solved by several methods, including branch and bound revised simplex and primal dual-simplex in combination with interior point algorithms. To solve the problem of depth-one-unobservability (DoOU), a nonlinear program is proposed using Sequential Quadratic Programming (SQP), Interior-Point methods (IPMs) or YALMIP\s branch-and-bound algorithm. Additionally, the paper proposes the use of metaheuristic algorithms, such as Genetic Algorithms (GAs) and Binary Particle Swarm Optimization (BPSO), to solve optimization problems under incomplete observability. The proposed algorithms are tested using simulations on IEEE standard systems to illustrate their efficiency and reliability in solving the optimization problem under partial observability. Overall, the paper concludes that these algorithms can efficiently lead to the optimum point in a reasonable runtime. Hence, this work examines the problem of putting a restricted PMUs number to make the DoOU and to give a feedback to the state estimation routine accuracy. © 2024 Institute of Physics Publishing. All rights reserved.en_US
dc.language.isoenen_US
dc.publisherJournal of Physics: Conference Seriesen_US
dc.publisherInstitute of Physicsen_US
dc.subjectAlgorithmsen_US
dc.subjectMathematicsen_US
dc.subjectSequential Quadratic Programmingen_US
dc.subjectGenetic Algorithmsen_US
dc.subjectBinary Particle Swarm Optimizationen_US
dc.titleAn Incomplete Observability-Constrained Pmu Allocation Problem By Using Mathematical and Evolutionary Algorithmsen_US
dc.typeArticleen_US
Appears in Collections:Conference Papers

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