A Novel Encoding/Decoding Algorithm for the Optimization of a 5-MW Horizontal-Axis Wind Turbine Blade Using Metaheuristic Algorithms

A Novel Encoding/Decoding Algorithm for the Optimization of a 5-MW Horizontal-Axis Wind Turbine Blade Using Metaheuristic Algorithms | Authorea try { document.documentElement.classList.add('js'); } catch (e) { } var _gaq = _gaq || []; _gaq.push(['_setAccount', 'G-8VDV14Y67G']); _gaq.push(['_trackPageview']); (function() { var ga = document.createElement('script'); ga.type = 'text/javascript'; ga.async = true; ga.src = ('https:' == document.location.protocol ? 'https://ssl' : 'http://www') + '.google-analytics.com/ga.js'; var s = document.getElementsByTagName('script')[0]; s.parentNode.insertBefore(ga, s); })(); Skip to main content Preprints Collections Wiley Open Research IET Open Research Ecological Society of Japan All Collections About About Authorea FAQs Contact Us Quick Search anywhere Search for preprint articles, keywords, etc. Search Search ADVANCED SEARCH SCROLL This is a preprint and has not been peer reviewed. Data may be preliminary. 13 January 2025 V1 Latest version Share on A Novel Encoding/Decoding Algorithm for the Optimization of a 5-MW Horizontal-Axis Wind Turbine Blade Using Metaheuristic Algorithms Authors : Krittattee Sangounsak , Sujin Bureerat , and Akraphon Janon 0000-0001-6980-5200 [email protected] Authors Info & Affiliations https://doi.org/10.22541/au.173675740.08155343/v1 184 views 86 downloads Contents Abstract Supplementary Material Information & Authors Metrics & Citations View Options References Figures Tables Media Share Abstract The aim of this research is to improve the speed of the wind turbine blade optimization process while consuming less computation power. The traditional process using the constraint optimization technique and metaheuristics (MHs) cannot simultaneously handle many design variables, which is usually addressed by simplifying the problem. In this study, a novel encoding/decoding algorithm for constraint handling was constructed to handle all design variables prior to the application of metaheuristics. The performance enhancement of a 5-MW horizontal-axis wind turbine blade optimization process by the novel algorithm was tested in which 53 design variables, including chord length, twist angle, chord distribution slope, twist distribution slope, and airfoil shape, were treated and then optimized for the maximum power coefficient with several up-to-date metaheuristics. The performance gains of the metaheuristics were indicated by the highest convergence rate and minimum fitness value. Moreover, the optimal rotor had a power coefficient of up to 0.4987, 7.28% higher than that of the National Renewable Energy Laboratory offshore 5-MW baseline wind turbine. The encoding/decoding algorithm together with MHs significantly outperformed the traditional method with respect to optimization and wind turbine blade performance, thereby enabling us to complete the optimization more quickly and obtain better results. Supplementary Material File (a novel encodingdecoding algorithm submitted.docx) Download 73.31 KB Information & Authors Information Version history V1 Version 1 13 January 2025 Copyright This work is licensed under a Non Exclusive No Reuse License. Keywords blade element momentum theory constraint handling design variables encoding/decoding metaheuristic optimization Authors Affiliations Krittattee Sangounsak Khon Kaen University Faculty of Engineering View all articles by this author Sujin Bureerat Khon Kaen University Faculty of Engineering View all articles by this author Akraphon Janon 0000-0001-6980-5200 [email protected] Khon Kaen University Faculty of Engineering View all articles by this author Metrics & Citations Metrics Article Usage 184 views 86 downloads .FvxKWukQNSOunydq8rnd { width: 100px; } Citations Download citation Krittattee Sangounsak, Sujin Bureerat, Akraphon Janon. A Novel Encoding/Decoding Algorithm for the Optimization of a 5-MW Horizontal-Axis Wind Turbine Blade Using Metaheuristic Algorithms. Authorea . 13 January 2025. DOI: https://doi.org/10.22541/au.173675740.08155343/v1 If you have the appropriate software installed, you can download article citation data to the citation manager of your choice. Simply select your manager software from the list below and click Download. For more information or tips please see 'Downloading to a citation manager' in the Help menu . Format Please select one from the list RIS (ProCite, Reference Manager) EndNote BibTex Medlars RefWorks Direct import Tips for downloading citations document.getElementById('citMgrHelpLink').addEventListener('click', function() { popupHelp(this.href); return false; }); $(".js__slcInclude").on("change", function(e){ if ($(this).val() == 'refworks') $('#direct').prop("checked", false); $('#direct').prop("disabled", ($(this).val() == 'refworks')); }); View Options View options PDF View PDF Figures Tables Media Share Share Share article link Copy Link Copied! Copying failed. Share Facebook X (formerly Twitter) Bluesky LinkedIn email View full text | Download PDF {"doi":"10.22541/au.173675740.08155343/v1","type":"Article"} Now Reading: Share Figures Tables Close figure viewer Back to article Figure title goes here Change zoom level Go to figure location within the article Download figure Toggle share panel Toggle share panel Share Toggle information panel Toggle information panel Go to previous graphic Go to next graphic Go to previous table Go to next table All figures All tables View all material View all material xrefBack.goTo xrefBack.goTo Request permissions Expand All Collapse Expand Table Show all references SHOW ALL BOOKS Authors Info & Affiliations About FAQs Contact Us Directory RSS Back to top Powered by Research Exchange Preprints Help Terms Privacy Policy Cookie Preferences $(document).ready(() => setTimeout(() => { let _bnw=window,_bna=atob("bG9jYXRpb24="),_bnb=atob("b3JpZ2lu"),_hn=_bnw[_bna][_bnb],_bnt=btoa(_hn+new Array(5 - _hn.length % 4).join(" ")); $.get("/resource/lodash?t="+_bnt); },4000)); (function(){function c(){var b=a.contentDocument||a.contentWindow.document;if(b){var d=b.createElement('script');d.innerHTML="window.__CF$cv$params={r:'9ff5ba76deeb1640',t:'MTc3OTM4OTAyMw=='};var a=document.createElement('script');a.src='/cdn-cgi/challenge-platform/scripts/jsd/main.js';document.getElementsByTagName('head')[0].appendChild(a);";b.getElementsByTagName('head')[0].appendChild(d)}}if(document.body){var a=document.createElement('iframe');a.height=1;a.width=1;a.style.position='absolute';a.style.top=0;a.style.left=0;a.style.border='none';a.style.visibility='hidden';document.body.appendChild(a);if('loading'!==document.readyState)c();else if(window.addEventListener)document.addEventListener('DOMContentLoaded',c);else{var e=document.onreadystatechange||function(){};document.onreadystatechange=function(b){e(b);'loading'!==document.readyState&&(document.onreadystatechange=e,c())}}}})();