Distributed Control of Large-Scale Offshore Wind Farms

Project Details

Description

A key socio-economic challenge for Europe is: how to deal with a climate change, while meeting rapidly increasing demand for energy and ensuring security of supply? Wind energy can be a significant part of the answer. The new frontier of the wind industry is large-scale offshore wind farms. While promising, considerable research and development tasks remain to be carried out before it reaches its full potential in terms of the efficient, stable, safe, predictable and controllable supply of energy. Closed loop control of wind power installations has historically been decentralized and a collection of wind turbines in farms is a highly complex system with interdependencies through the shared resource, the wind. Wind turbines are affected by the wind but they also changes the wind field within the farm through the control. To address objectives related to cost, quality of power and mechanical loads, models and control paradigms must be developed that allow wind resource allocation to individual turbines.

 

Inspired by the industrial case of complex large-scale distributed offshore wind farms, the Aeolus project will research and develop models that allow real-time predictions of flows and incorporate measurements from a set of spatially distributed sensor devices. In Aeolus we will use the flow information as a basis for new control paradigms, centralized and distributed that acknowledges the uncertainty in the modelling and dynamically manages the flow resource in order to optimise specific control objectives. The model and control principles are used for control of a wind power farm to increase energy quality and reduce the fatigue loads. The usefulness of our techniques will be validated on a case study and by physical experiments on a scaled wind power farm.

AcronymAEOLUS
StatusFinished
Effective start/end date01/05/200830/04/2010

Funding

  • ICT - INFORMATION AND COMMUNICATION TECHNOLOGIES

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  • Research Output

    On Using Wind Speed Preview to Reduce Wind Turbine Tower Oscillations

    Kristalny, M., Madjidian, D. & Knudsen, T., 2013, In : I E E E Transactions on Control Systems Technology. 21, 4, p. 1191-1198 8 p.

    Research output: Contribution to journalJournal articleResearchpeer-review

  • 22 Citations (Scopus)

    Data Driven Modelling of the Dynamic Wake Between Two Wind Turbines

    Knudsen, T. & Bak, T., 2012, 16th IFAC Symposium on System Identification. Elsevier, Vol. 16. p. 1677-1682 6 p. (I F A C Workshop Series).

    Research output: Contribution to book/anthology/report/conference proceedingArticle in proceedingResearchpeer-review

    File
  • 4 Citations (Scopus)
    424 Downloads (Pure)

    Fatigue distribution optimization for offshore wind farms using intelligent agent control

    Zhao, R., Shen, W., Knudsen, T. & Bak, T., 2012, In : Wind Energy. 15, 7, p. 927-944 18 p.

    Research output: Contribution to journalJournal articleResearchpeer-review

    File
  • 11 Citations (Scopus)
    781 Downloads (Pure)