Optimal Wind Turbines Micrositing in Onshore Wind Farms Using Fuzzy Genetic Algorithm
With the fast growth in the number and size of installed wind farms (WFs) around the world, optimal wind turbines (WTs) micrositing has become a challenge from both technological and mathematical points of view. An appropriate layout of wind turbines is crucial to obtain adequate performance with re...
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| Veröffentlicht in: | Mathematical problems in engineering 2015-01, Vol.2015 (2015), p.1-9 |
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| container_title | Mathematical problems in engineering |
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| creator | Zhang, Huaguang Sun, Qiuye Zhang, Rui Yang, Jun |
| description | With the fast growth in the number and size of installed wind farms (WFs) around the world, optimal wind turbines (WTs) micrositing has become a challenge from both technological and mathematical points of view. An appropriate layout of wind turbines is crucial to obtain adequate performance with respect to the development and operation of the wind power plant during its life span. This work presents a fuzzy genetic algorithm (FGA) for maximizing the economic profitability of the project. The algorithm considers a new WF model including several important factors to the design of the layout. The model consists of wake loss, terrain effect, and economic benefits, which can be calculated by locations of wind turbines. The results demonstrate that the algorithm performs better than genetic algorithm, in terms of maximum values of net annual value of wind power plants and computational burden. |
| doi_str_mv | 10.1155/2015/324203 |
| format | Article |
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An appropriate layout of wind turbines is crucial to obtain adequate performance with respect to the development and operation of the wind power plant during its life span. This work presents a fuzzy genetic algorithm (FGA) for maximizing the economic profitability of the project. The algorithm considers a new WF model including several important factors to the design of the layout. The model consists of wake loss, terrain effect, and economic benefits, which can be calculated by locations of wind turbines. The results demonstrate that the algorithm performs better than genetic algorithm, in terms of maximum values of net annual value of wind power plants and computational burden.</description><identifier>ISSN: 1024-123X</identifier><identifier>EISSN: 1563-5147</identifier><identifier>DOI: 10.1155/2015/324203</identifier><language>eng</language><publisher>Cairo, Egypt: Hindawi Publishing Corporation</publisher><subject>Alternative energy sources ; Design factors ; Economics ; Fuzzy ; Fuzzy logic ; Fuzzy set theory ; Genetic algorithms ; Mathematical models ; Methods ; Monte Carlo simulation ; Mutation ; Optimization ; Optimization algorithms ; Plant layout ; Power plants ; Studies ; Turbines ; Wind effects ; Wind farms ; Wind power ; Wind turbines</subject><ispartof>Mathematical problems in engineering, 2015-01, Vol.2015 (2015), p.1-9</ispartof><rights>Copyright © 2015 Jun Yang et al.</rights><rights>Copyright © 2015 Jun Yang et al. Jun Yang et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.</rights><lds50>peer_reviewed</lds50><oa>free_for_read</oa><woscitedreferencessubscribed>false</woscitedreferencessubscribed><citedby>FETCH-LOGICAL-c422t-2b405486c04b3742e00dbc3214bd740edb83118f6e8dfb41eb44f0c31ec3d0013</citedby><cites>FETCH-LOGICAL-c422t-2b405486c04b3742e00dbc3214bd740edb83118f6e8dfb41eb44f0c31ec3d0013</cites><orcidid>0000-0002-1303-1370 ; 0000-0003-0599-1416 ; 0000-0001-8801-0884</orcidid></display><links><openurl>$$Topenurl_article</openurl><openurlfulltext>$$Topenurlfull_article</openurlfulltext><thumbnail>$$Tsyndetics_thumb_exl</thumbnail><link.rule.ids>314,776,780,27901,27902</link.rule.ids></links><search><contributor>Sreeram, Victor</contributor><creatorcontrib>Zhang, Huaguang</creatorcontrib><creatorcontrib>Sun, Qiuye</creatorcontrib><creatorcontrib>Zhang, Rui</creatorcontrib><creatorcontrib>Yang, Jun</creatorcontrib><title>Optimal Wind Turbines Micrositing in Onshore Wind Farms Using Fuzzy Genetic Algorithm</title><title>Mathematical problems in engineering</title><description>With the fast growth in the number and size of installed wind farms (WFs) around the world, optimal wind turbines (WTs) micrositing has become a challenge from both technological and mathematical points of view. 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An appropriate layout of wind turbines is crucial to obtain adequate performance with respect to the development and operation of the wind power plant during its life span. This work presents a fuzzy genetic algorithm (FGA) for maximizing the economic profitability of the project. The algorithm considers a new WF model including several important factors to the design of the layout. The model consists of wake loss, terrain effect, and economic benefits, which can be calculated by locations of wind turbines. 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| source | Wiley Online Library Open Access; Elektronische Zeitschriftenbibliothek - Frei zugängliche E-Journals; Alma/SFX Local Collection |
| subjects | Alternative energy sources Design factors Economics Fuzzy Fuzzy logic Fuzzy set theory Genetic algorithms Mathematical models Methods Monte Carlo simulation Mutation Optimization Optimization algorithms Plant layout Power plants Studies Turbines Wind effects Wind farms Wind power Wind turbines |
| title | Optimal Wind Turbines Micrositing in Onshore Wind Farms Using Fuzzy Genetic Algorithm |
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