Wind farm layout design using cuckoo search algorithm

dc.contributor.authorRehman, S.
dc.contributor.authorAli, S.S.
dc.contributor.authorKhan, Salman Ahmad
dc.date.accessioned2018-11-13T14:07:38Z
dc.date.issued2018
dc.description.abstractWind energy has emerged as a strong alternative to fossil fuels for power generation. To generate this energy, wind turbines are placed in a wind farm. The extraction of maximum energy from these wind farms requires optimal placement of wind turbines. Due to complex nature of micrositing of wind turbines, the wind farm layout design problem is considered a complex optimization problem. In the recent past, various techniques and algorithms have been developed for optimization of energy output from wind farms. The present study proposes an optimization approach based on the cuckoo search (CS) algorithm, which is relatively a recent technique. A variant of CS is also proposed that incorporates a heuristic-based seed solution for better performance. The proposed CS algorithms are compared with genetic and particle swarm optimization algorithms which have been extensively applied to wind farm layout design. Empirical results indicate that the proposed CS algorithms outperformed the genetic and particle swarm optimization algorithms for the given test scenarios in terms of yearly power output and efficiency.en_ZA
dc.description.departmentComputer Scienceen_ZA
dc.description.embargo2019-10-11
dc.description.librarianhj2018en_ZA
dc.description.sponsorshipDeanship of Research at King Fahd University of Petroleum and Minerals under project number IN131012.en_ZA
dc.description.urihttp://www.tandfonline.com/loi/uaai20en_ZA
dc.identifier.citationS. Rehman, S. S. Ali & S. A. Khan (2018): Wind Farm Layout Design Using Cuckoo Search Algorithms, Applied Artificial Intelligence, DOI: 10.1080/08839514.2018.1525521. NYP.en_ZA
dc.identifier.issn0883-9514 (print)
dc.identifier.issn1087-6545 (online)
dc.identifier.other10.1080/08839514.2018.1525521
dc.identifier.urihttp://hdl.handle.net/2263/67250
dc.language.isoenen_ZA
dc.publisherTaylor and Francisen_ZA
dc.rights© 2018 Taylor & Francis. This is an electronic version of an article published in Applied Artificial Intelligence, vol. x, no. y, pp. z-zz, 2018. doi : 10.1080/08839514.2018.1525521. Applied Artificial Intelligence is available online at : http://www.tandfonline.com/loi/uaai20.en_ZA
dc.subjectCuckoo search (CS)en_ZA
dc.subjectParticle swarm optimization (PSO)en_ZA
dc.subjectWind energyen_ZA
dc.subjectWind farmen_ZA
dc.subjectOptimization approachen_ZA
dc.subjectAlgorithmen_ZA
dc.subjectElectric utilitiesen_ZA
dc.subjectFossil fuelsen_ZA
dc.subjectWind turbinesen_ZA
dc.subjectAlternative to fossil fuelsen_ZA
dc.subjectComplex optimization problemsen_ZA
dc.subjectCuckoo search algorithmsen_ZA
dc.subjectOptimal placementsen_ZA
dc.subjectWind farm layoutsen_ZA
dc.subjectWind poweren_ZA
dc.titleWind farm layout design using cuckoo search algorithmen_ZA
dc.typePostprint Articleen_ZA

Files

Original bundle

Now showing 1 - 1 of 1
Loading...
Thumbnail Image
Name:
Rehman_Wind_2018.pdf
Size:
2.35 MB
Format:
Adobe Portable Document Format
Description:
Postprint Article

License bundle

Now showing 1 - 1 of 1
Loading...
Thumbnail Image
Name:
license.txt
Size:
1.75 KB
Format:
Item-specific license agreed upon to submission
Description: