Fitness-distance balance based artificial ecosystem optimisation to solve transient stability constrained optimal power flow problem

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dc.authoridDuman, Serhat/0000-0002-1091-125X
dc.authoridKAHRAMAN, Hamdi Tolga/0000-0001-9985-6324
dc.authoridsonmez, yusuf/0000-0002-9775-9835
dc.authorwosidDuman, Serhat/O-9406-2014
dc.authorwosidKAHRAMAN, Hamdi Tolga/AAW-5335-2020
dc.authorwosidsonmez, yusuf/J-4733-2014
dc.contributor.authorSönmez, Yusuf
dc.contributor.authorDuman, Serhat
dc.contributor.authorKahraman, Hamdi T.
dc.contributor.authorKati, Mehmet
dc.contributor.authorAras, Sefa
dc.contributor.authorGüvenç, Uğur
dc.date.accessioned2023-07-26T11:51:19Z
dc.date.available2023-07-26T11:51:19Z
dc.date.issued2022
dc.departmentDÜ, Mühendislik Fakültesi, Elektrik-Elektronik Mühendisliği Bölümüen_US
dc.description.abstractThe Transient Stability Constrained Optimal Power Flow (TSCOPF) has become an important tool for power systems today. TSCOPF is a nonlinear optimisation problem, making its solution difficult, especially for small power systems. This paper presents a new optimisation method that incorporates Fitness-Distance Balance (FDB) with the Artificial Ecosystem Optimisation (AEO) algorithm to improve the solution quality in multi-dimensional and nonlinear optimisation problems. The proposed method, named the Fitness-Distance Balance Artificial Ecosystem Optimisation (FDBAEO), also has the capacity to solve the TSCOPF problem efficiently. In order to evaluate the proposed algorithm, it was tested on IEEE CEC benchmarks and on an IEEE 30-bus test system for the TSCOPF problem. Simulation results were compared with the basic AEO algorithm and other current meta-heuristic methods reported in the literature. The results showed that the proposed method was more effective in converging at the global optimum point in solving the TSCOPF problem compared to the other algorithms. This situation indicates that the design changes made in the decomposition phase of the AEO were more suitable for simulating the operation of the algorithm in the real world. The FDBAEO has exhibited a promising performance in solving both single-objective optimisation and constrained real-world engineering design problems.en_US
dc.identifier.doi10.1080/0952813X.2022.2104388
dc.identifier.issn0952-813X
dc.identifier.issn1362-3079
dc.identifier.scopus2-s2.0-85134943600en_US
dc.identifier.scopusqualityQ3en_US
dc.identifier.urihttps://doi.org/10.1080/0952813X.2022.2104388
dc.identifier.urihttps://hdl.handle.net/20.500.12684/12537
dc.identifier.wosWOS:000830378900001en_US
dc.identifier.wosqualityQ3en_US
dc.indekslendigikaynakWeb of Scienceen_US
dc.indekslendigikaynakScopusen_US
dc.institutionauthorGüvenç, Uğur
dc.language.isoenen_US
dc.publisherTaylor & Francis Ltden_US
dc.relation.ispartofJournal of Experimental & Theoretical Artificial Intelligenceen_US
dc.relation.publicationcategoryMakale - Uluslararası Hakemli Dergi - Kurum Öğretim Elemanıen_US
dc.rightsinfo:eu-repo/semantics/closedAccessen_US
dc.snmz$2023V1Guncelleme$en_US
dc.subjectTscopf Problem; Modern Power System; Power System Planning; Fitness-Distance Balance (Fdb); Artificial Ecosystem Optimisationen_US
dc.subjectInterior-Point Method; Bee Colony Algorithm; Differential Evolution; Securityen_US
dc.titleFitness-distance balance based artificial ecosystem optimisation to solve transient stability constrained optimal power flow problemen_US
dc.typeArticleen_US

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