A novel sea horse optimizer based load frequency controller for two-area power system with PV and thermal units

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dc.authorscopusid57818691400en_US
dc.authorscopusid57818751600en_US
dc.authorscopusid48661591200en_US
dc.authorscopusid35103037800en_US
dc.contributor.authorAndic, C.
dc.contributor.authorOzumcan, S.
dc.contributor.authorVaran, M.
dc.contributor.authorOzturk, A.
dc.date.accessioned2024-08-23T16:07:28Z
dc.date.available2024-08-23T16:07:28Z
dc.date.issued2024en_US
dc.departmentDüzce Üniversitesien_US
dc.description.abstractThis study introduces the Sea Horse Optimizer (SHO), a novel optimization algorithm designed for Load Frequency Control (LFC) in two-area power systems including photovoltaic and thermal units. Inspired by the interactive behaviors of seahorses, this population-based metaheuristic algorithm leverages strategies like Brownian motion and Levy flights to efficiently search for optimal solutions, demonstrating quicker and more stable identification of global and local optima than traditional algorithms. The proposed SHO algorithm was tested in a two-region power system containing a photovoltaic system and a reheat thermal unit under three different scenarios. In the first scenario, the frequency response of the algorithm to a 0.1 p.u. load change in both regions was examined. In the second scenario, the algorithm's frequency response to sudden load changes from 0.1 p.u. to 0.4 p.u. was tested. Finally, the algorithm's frequency response was examined against different levels of solar irradiance for sensitivity analysis. This study compared the performance of the SHO-optimized controller with the optimization algorithms reported in the literature, including the Genetic Algorithm (GA), Firefly Algorithm (FA), Whale Optimization Algorithm (WOA), and Modified Whale Optimization Algorithm (MWOA). In this context, the optimization of PI controller gain parameters based on the ITAE metric resulted in SHO algorithm achieving the best performance with values of 2.5308, followed by WOA at 4.1211, FA at 7.4259, and GA at 12.1244. In tests, SHO significantly outperformed these algorithms in key performance metrics, such as Settling Time, Overshoot (M+), and Undershoot (M-). Specifically, SHO achieved 98.94% better overshoot and 85.25% reduced undershoot than GA, and concluded settling times 52.79% faster than GA in the first scenario. Similar superior outcomes were noted in subsequent tests. These results underline SHO's efficacy in enhancing system stability and control performance, marking it as a significant advancement over conventional LFC methods. © 2024, Association for Scientific Computing Electronics and Engineering (ASCEE). All rights reserved.en_US
dc.identifier.doi10.31763/ijrcs.v4i2.1341
dc.identifier.endpage627en_US
dc.identifier.issn2775-2658
dc.identifier.issue2en_US
dc.identifier.scopus2-s2.0-85196810958en_US
dc.identifier.scopusqualityN/Aen_US
dc.identifier.startpage606en_US
dc.identifier.urihttps://doi.org/10.31763/ijrcs.v4i2.1341
dc.identifier.urihttps://hdl.handle.net/20.500.12684/14658
dc.identifier.volume4en_US
dc.indekslendigikaynakScopusen_US
dc.language.isoenen_US
dc.publisherAssociation for Scientific Computing Electronics and Engineering (ASCEE)en_US
dc.relation.ispartofInternational Journal of Robotics and Control Systemsen_US
dc.relation.publicationcategoryMakale - Uluslararası Hakemli Dergi - Kurum Öğretim Elemanıen_US
dc.rightsinfo:eu-repo/semantics/closedAccessen_US
dc.subjectLoad Frequency Controlen_US
dc.subjectPV Systemen_US
dc.subjectSea Horse Optimizeren_US
dc.subjectTwo-are Power Systemen_US
dc.titleA novel sea horse optimizer based load frequency controller for two-area power system with PV and thermal unitsen_US
dc.typeArticleen_US

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