A robust crow search algorithm-based power system stabilizer for the SMIB system

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dc.authoridYokuş, Hamdullah/0000-0002-5259-3181
dc.contributor.authorYokuş, Hamdullah
dc.contributor.authorÖztürk, Ali
dc.date.accessioned2023-07-26T11:58:44Z
dc.date.available2023-07-26T11:58:44Z
dc.date.issued2022
dc.departmentDÜ, Mühendislik Fakültesi, Elektrik-Elektronik Mühendisliği Bölümüen_US
dc.description.abstractPower system stabilizers (PSSs) are extensively used in generator units to enhance the transient stability of the power system. Hence, optimal tuning and placement of the parameters of PSS are crucial for the efficiency of the stabilizer. Researchers have been proposed many methods for optimizing such parameters. In this paper, the Crow search algorithm (CSA), which is based on the intelligence of crows, was employed in a single-machine infinite-bus (SMIB) system to determine the optimum parameters of the PSS. Modeling and simulation of the SMIB and designing of PSS were made by MATLAB/Simulink. PSSs are designed to minimize low-frequency oscillations such as power angle, rotor speed, and field current deviation following a large disturbance. Our objective in this study is the minimization of the rotor speed deviation. The results of the simulations proved the effectiveness and robustness of the optimization process compared to other metaheuristic algorithms such as particle swarm optimization (PSO) and genetic algorithm (GA). When compared to the performance attained by the GA-based and PSO-based PSS controller designs, the simulations show that the CSA-based PSS delivers a far better dynamic response when the system is disrupted. The CSA-based PSS settles 48.1% faster than the PSO-based PSS and 55.7% faster than the GA-based PSS. CSA has just 2 parameters to adjust, making it much easier to implement than other methods. These parameters for PSO and GA are 4 and 6. When CSA-based PSS is used in the SMIB system, overshoot and low-frequency oscillations are also significantly reduced compared to other methods.en_US
dc.identifier.doi10.1007/s00521-022-06943-w
dc.identifier.endpage9173en_US
dc.identifier.issn0941-0643
dc.identifier.issn1433-3058
dc.identifier.issue11en_US
dc.identifier.scopus2-s2.0-85127949155en_US
dc.identifier.scopusqualityQ1en_US
dc.identifier.startpage9161en_US
dc.identifier.urihttps://doi.org/10.1007/s00521-022-06943-w
dc.identifier.urihttps://hdl.handle.net/20.500.12684/13557
dc.identifier.volume34en_US
dc.identifier.wosWOS:000783461800001en_US
dc.identifier.wosqualityQ2en_US
dc.indekslendigikaynakWeb of Scienceen_US
dc.indekslendigikaynakScopusen_US
dc.institutionauthorYokuş, Hamdullah
dc.institutionauthorÖztürk, Ali
dc.language.isoenen_US
dc.publisherSpringer London Ltden_US
dc.relation.ispartofNeural Computing & Applicationsen_US
dc.relation.publicationcategoryMakale - Uluslararası Hakemli Dergi - Kurum Öğretim Elemanıen_US
dc.rightsinfo:eu-repo/semantics/closedAccessen_US
dc.snmz$2023V1Guncelleme$en_US
dc.subjectCrow Search Algorithm; Genetic Algorithm; Power System Stabilizer; Power System Transient Stabilityen_US
dc.subjectOutput-Feedback; Design; Fuzzy; Optimization; Pssen_US
dc.titleA robust crow search algorithm-based power system stabilizer for the SMIB systemen_US
dc.typeArticleen_US

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