Change search
CiteExportLink to record
Permanent link

Direct link
Cite
Citation style
  • apa
  • ieee
  • modern-language-association-8th-edition
  • vancouver
  • Other style
More styles
Language
  • de-DE
  • en-GB
  • en-US
  • fi-FI
  • nn-NO
  • nn-NB
  • sv-SE
  • Other locale
More languages
Output format
  • html
  • text
  • asciidoc
  • rtf
Development of a pitch based wake optimisation control strategy to improve total farm power production
Uppsala University, Disciplinary Domain of Science and Technology, Earth Sciences, Department of Earth Sciences.
2016 (English)Independent thesis Advanced level (degree of Master (One Year)), 10 credits / 15 HE creditsStudent thesis
Abstract [en]

In this thesis, the effect of pitch based optimisation was explored for a 80 turbine wind farm. Using a modified Jensen wake model and the Particle Swarm Optimisation (PSO) model, a pitch optimisation strategy was created for the dominant turbulence and atmospheric condition for the wind farm.

As the wake model was based on the FLORIS model developed by P.M.O Gebraad et. al., the wake and power model was compared with the FLORIS model and a -0.090% difference was found. To determine the dynamic predictive capability of the wake model, measurement values across a 10 minute period for a 19 wind turbine array were used and the wake model under predicted the power production by 17.55%. Despite its poor dynamic predictive capability, the wake model was shown to accurately match the AEP production of the wind farm when compared to a CFD simulation done in FarmFlow and only gave a 3.10% over-prediction.

When the optimisation model was applied with 150 iterations and particles, the AEP production of the wind farm increased by 0.1052%, proving that the pitch optimisation method works for the examined wind farm. When the iterations and particles used for the optimisation was increased to 250, the power improvement between optimised results improved by 0.1144% at a 222.5% increase in computational time, suggesting that the solution has yet to fully converge. While the solutions did not fully converge, they converged sufficiently and an increase in iterations gave diminishing results. From the results, the pitch optimisation model was found to give a significant increase in power production, especially in wake intensive wind directions. However, the dynamic predictive capabilities will have be improved upon before the control strategy can be applied to an operational wind farm.

Place, publisher, year, edition, pages
2016. , p. 110
Keywords [en]
Wake reduction, Jensen Wake Model, Particle Swarm Optimisation, Wind turbine Control Algorithm, Deterministic Wake Modelling, Heuristic Optimisation model
National Category
Energy Systems
Identifiers
URN: urn:nbn:se:uu:diva-304705OAI: oai:DiVA.org:uu-304705DiVA, id: diva2:1033621
External cooperation
Xinjiang Goldwind Science & Technology Co., Ltd
Educational program
Master Programme in Wind Power Project Management
Presentation
2016-08-30, 11:30 (English)
Supervisors
Examiners
Available from: 2017-03-22 Created: 2016-10-07 Last updated: 2017-03-22Bibliographically approved

Open Access in DiVA

fulltext(11032 kB)230 downloads
File information
File name FULLTEXT01.pdfFile size 11032 kBChecksum SHA-512
1b4c3c42b19ecec1013737cc04ddf088355b7b953fb4e89366c490519f6c5b968bc93d2967808f33e68c079271a41339925a3b830e70b95bfeccacecaa579ae2
Type fulltextMimetype application/pdf

By organisation
Department of Earth Sciences
Energy Systems

Search outside of DiVA

GoogleGoogle Scholar
Total: 230 downloads
The number of downloads is the sum of all downloads of full texts. It may include eg previous versions that are now no longer available

urn-nbn

Altmetric score

urn-nbn
Total: 477 hits
CiteExportLink to record
Permanent link

Direct link
Cite
Citation style
  • apa
  • ieee
  • modern-language-association-8th-edition
  • vancouver
  • Other style
More styles
Language
  • de-DE
  • en-GB
  • en-US
  • fi-FI
  • nn-NO
  • nn-NB
  • sv-SE
  • Other locale
More languages
Output format
  • html
  • text
  • asciidoc
  • rtf