Framework of Multi-objective Wind Farm Controller Applicable to Real Wind Farms

Research output: Chapter in Book/Report/Conference proceedingArticle in proceedings – Annual report year: 2016Researchpeer-review

View graph of relations

Optimal wind farm control can mitigate adverse wake effects that can potentially cause up to 40%
power loss and 80% increased fatigue loads in wind farms. The aim of this work is to outline a
methodological framework of an optimal wind farm controller, which provides improved solutions to
critical areas of optimal wind farm control research. The basis of this framework is a review of
optimal wind farm control methodologies, which is presented first. It is observed that there is, at
present, mainly a need for more advanced wind farm operation models. Thereafter the framework of
a multi-objective optimal wind farm controller is outlined with the following key characteristics.
Available control objectives are (i) to maximize the total wind farm power output or (ii) to follow a
specified power reference for the wind farm’s total power output while reducing the fatigue loads of
the wind turbines in the wind farm. The controller design provides improved solutions for the
modelling of wind farm aerodynamics and turbine operation, that is the PossPOW algorithm and a
HAWC2-based turbine model, respectively. Moreover, all components of the framework are
designed as to enable the applicability of the controller to real wind farms
Original languageEnglish
Title of host publicationProceedings of Wind Europe Summit 2016
Number of pages10
Publication date2016
Publication statusPublished - 2016
EventWind Europe Summit 2016 - Congress Center Hamburg, Hamburg, Germany
Duration: 26 Sep 201629 Sep 2016
https://windeurope.org/summit2016/conference/

Conference

ConferenceWind Europe Summit 2016
LocationCongress Center Hamburg
CountryGermany
CityHamburg
Period26/09/201629/09/2016
Internet address

    Research areas

  • Optimal wind farm control, Review, Power maximisation, Fatigue reduction, Operation modelling

ID: 126633170