Skip to Main content Skip to Navigation
Journal articles

Synthesis of compact wind profiles using evolutionary algorithms

Abstract : In this paper, the authors face the problem of wind speed processing as environmental variable of a wind turbine system. Generally, the information on wind speed measurements is processed over long periods of time to be relevant with respect to the site characteristics (average and maximum speeds, statistics). Subsequent large scale profiles of wind speed lead to long processing time for simulation analysis and especially for optimization design that penalizes the search of optimal solutions. An original synthesis approach of a compact and representative wind speed profile using an Evolutionary Algorithm (EA) is proposed. This approach is compared to a purely statistical approach based on random number generators. It allows reducing the actual wind profile duration with compression ratios greater (two months of wind speed measurements are compressed in only 1 hour). Then, the synthesis approach by EA is applied to the sizing of an autonomous hybrid system based on wind turbine with battery storage for stand-alone energy systems. It has proven its effectiveness in reducing 200 days of wind speed measurements in only 10 days, allowing sizing the storage system with a significant gain in terms of computing time in the framework of the optimization process.
Document type :
Journal articles
Complete list of metadatas

Cited literature [30 references]  Display  Hide  Download

https://hal.archives-ouvertes.fr/hal-01061336
Contributor : Open Archive Toulouse Archive Ouverte (oatao) <>
Submitted on : Friday, September 5, 2014 - 3:02:59 PM
Last modification on : Wednesday, July 1, 2020 - 2:16:02 PM
Long-term archiving on: : Saturday, December 6, 2014 - 11:42:07 AM

File

Jaafar_10288.pdf
Files produced by the author(s)

Identifiers

Collections

Citation

Amine Jaafar, Malek Belouda, Bruno Sareni, Xavier Roboam, Jamel Belhadj. Synthesis of compact wind profiles using evolutionary algorithms. Inverse Problems in Science and Engineering, Taylor & Francis, 2013, pp. 1-19. ⟨10.1080/17415977.2013.823414⟩. ⟨hal-01061336⟩

Share

Metrics

Record views

193

Files downloads

349