Optimization of Wind Turbine Location and Sizing for Loss Minimization and Voltage Profile Enhancement Using Particle Swarm Optimization and Genetic Algorithms

Optimization of Wind Turbine Location and Sizing for Loss Minimization and Voltage Profile Enhancement Using Particle Swarm Optimization and Genetic Algorithms | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article Optimization of Wind Turbine Location and Sizing for Loss Minimization and Voltage Profile Enhancement Using Particle Swarm Optimization and Genetic Algorithms Taha Rachdi, Yahia Saoudi, Larbi Chrifi-Alaoui, Ayachi Errachdi This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-4933864/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract Numerous areas of power systems require finding solutions to nonlinear optimization issues, such as, the optimal location of wind turbines. In order to enhance the voltage profile and reduce line power losses. This research suggests two optimization techniques for figuring out the best wind turbine location in a distribution system. The suggested methodology based on particle swarm optimization (PSO) and genetic algorithm (GA) techniques to minimize the objective function. These algorithms are applied for IEEE 14 bus distribution system using MATLAB R2010a and the Power System Analysis Toolbox (PSAT). The results indicate that the obtained optimal values of the wind turbine location using particle swarm optimization technique are located at bus numbers 3, 6, 7, and 9, with a reduction in power losses of 85%. Additionally, the voltage profile across the system buses showed significant improvement, maintaining the voltage levels within permissible limits and closer to the nominal values. The genetic algorithm also provided effective results, demonstrating the robustness of both methods in addressing the optimization problem. Overall, this study highlights the potential of GA and PSO in enhancing the efficiency and reliability of power distribution systems by strategically integrating wind turbines. The comparative analysis between the two algorithms provides valuable insights into their performance, convergence characteristics, and computational efficiency, making them viable tools for modern power system optimization Wind Turbine Power Loss Minimization IEEE 14-bus Systems Genetic Algorithm (GA) Voltage Profile Enhancement Particle Swarm Optimization (PSO). Full Text Additional Declarations No competing interests reported. Cite Share Download PDF Status: Posted Version 1 posted You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. Our growing team is made up of researchers and industry professionals working together to solve the most critical problems facing scientific publishing. Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-4933864","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":347616952,"identity":"3e560dce-6a4b-4436-8f7b-4e7b7802535b","order_by":0,"name":"Taha Rachdi","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA6UlEQVRIiWNgGAWjYFACHigtwWBw4AOQZmMnqAFJy8EZIC3MpGhhBrMJabFnP3vwww+Ge/L8s5s3Hrb5tU2ej5mB8cPHHDy28OQlS/YwFBvOuHOs4HBu323DNmYGZsmZ2/A5LMdAgochgXGDRI7B4dye24xALWzMvPi08L8x/vmHIcEerMWy57Y9YS0SOWbSQFsSwVoYftxOJKzlxhszaxmDhOQZN9IKDvY23E5uY2ZsxusX9v4c45tvKhJs+2ckb/7w489t2/ntzQc/fMSjBQIMoDRjG5hsIKQeGfwhRfEoGAWjYBSMFAAAhcdNPbpaQH0AAAAASUVORK5CYII=","orcid":"","institution":"Tunis El Manar University","correspondingAuthor":true,"prefix":"","firstName":"Taha","middleName":"","lastName":"Rachdi","suffix":""},{"id":347616953,"identity":"ca11bae1-c10f-401c-92a1-021e4b85b805","order_by":1,"name":"Yahia Saoudi","email":"","orcid":"","institution":"Sfax University street of Soukra","correspondingAuthor":false,"prefix":"","firstName":"Yahia","middleName":"","lastName":"Saoudi","suffix":""},{"id":347616954,"identity":"9bf52f25-5b63-4f0f-aee4-91d9a69516ec","order_by":2,"name":"Larbi Chrifi-Alaoui","email":"","orcid":"","institution":"Jules Verne University","correspondingAuthor":false,"prefix":"","firstName":"Larbi","middleName":"","lastName":"Chrifi-Alaoui","suffix":""},{"id":347616955,"identity":"44886b13-0ae9-4aba-8f65-d076713275e3","order_by":3,"name":"Ayachi Errachdi","email":"","orcid":"","institution":"Tunis El Manar University","correspondingAuthor":false,"prefix":"","firstName":"Ayachi","middleName":"","lastName":"Errachdi","suffix":""}],"badges":[],"createdAt":"2024-08-18 14:51:01","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-4933864/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-4933864/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":63957724,"identity":"01ab942e-9c12-41d4-abc4-7484f79a3a9d","added_by":"auto","created_at":"2024-09-04 08:14:00","extension":"pdf","order_by":1,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":886139,"visible":true,"origin":"","legend":"","description":"","filename":"Paper.pdf","url":"https://assets-eu.researchsquare.com/files/rs-4933864/v1_covered_c25072e7-e21f-4239-90df-16336282b00d.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Optimization of Wind Turbine Location and Sizing for Loss Minimization and Voltage Profile Enhancement Using Particle Swarm Optimization and Genetic Algorithms","fulltext":[],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":false,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":true,"hideJournal":true,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":true,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":true,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"Wind Turbine, Power Loss Minimization, IEEE 14-bus Systems, Genetic Algorithm (GA), Voltage Profile Enhancement, Particle Swarm Optimization (PSO).","lastPublishedDoi":"10.21203/rs.3.rs-4933864/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-4933864/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eNumerous areas of power systems require finding solutions to nonlinear optimization issues, such as, the optimal location of wind turbines. In order to enhance the voltage profile and reduce line power losses. This research suggests two optimization techniques for figuring out the best wind turbine location in a distribution system. The suggested methodology based on particle swarm optimization (PSO) and genetic algorithm (GA) techniques to minimize the objective function. These algorithms are applied for IEEE 14 bus distribution system using MATLAB R2010a and the Power System Analysis Toolbox (PSAT). The results indicate that the obtained optimal values of the wind turbine location using particle swarm optimization technique are located at bus numbers 3, 6, 7, and 9, with a reduction in power losses of 85%. Additionally, the voltage profile across the system buses showed significant improvement, maintaining the voltage levels within permissible limits and closer to the nominal values. 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