CONWIND: Research on smart operation control technologies for offshore wind farms

Nicholas Doherty, , Nicholas doherty p ON Bh Dy O Fo M unsplash, <p><a href="https://unsplash.com/photos/pONBhDyOFoM">Unsplash</a></p>, Offshore windfarm

Source:
Nicholas Doherty

A wind farm's profitability is hugely influenced by the wakes generated by the turbines, which are moving turbulent wind fields with decreased average wind speed. The increased size in offshore wind turbines leads to larger wakes, and thus the steering of these wakes (by steering the turbines) can have a big impact on the energy production and the wear and tear on the turbines. The profitability of the wind farm is also linked to accurate production estimates or production goals, which are easier met through a smart and coordinated control of the turbines.

In order to improve current control algorithms of the turbines, an efficient and reliable prediction of the incoming wind field is needed. We are particularly interested in short term forecasting, ranging from 5 minutes to 1 hour. Improvements in the wind prediction will be investigated through the integration of measurements in the wind modelling as well as by the use of machine learning.

The next step is to obtain computationally efficient models that will evaluate the movement of the wakes in the wind farm and the impact on the turbines. In combination with the improved short-term forecasts we will then be able to obtain a wind farm model that will form the basis for the controller. We will use statistics to estimate the uncertain parameters that will be relevant for the wind farm behavior, and both physical models and machine learning techniques will be evaluated.

The control objectives are to reduce the severity of loading and to distribute accumulated fatigue evenly over the turbines, while maintaining or increasing the power production and complying with grid constraints. Based on the wind farm model and the incoming data, the controller will send the appropriate command to all the turbines in the farm.

Finally, the project will validate its findings through demonstrations in offshore wind farms or laboratories in China or Norway, or through numerical validations were appropriate.

Contact

Annette Fagerhaug Stephansen

Research Director Digital Systems - Bergen

anst@norceresearch.no
+47 402 23 815

Project facts

Name

CONWIND: Research on smart operation control technologies for offshore wind farms

Status

CONCLUDED

Duration

01.01.20 - 31.12.23

Location

Bergen

Total budget

25.000.000 NOK

Research group

Digital Systems

Coastal and Ocean Systems

Research Topics

Renewable energy

Offshore Wind Energy

Machine learning

Funding

Research Council of Norway (RCN)

Prosjekteier

NORCE

Project members

Annette Fagerhaug Stephansen

Vasileios Tsiolakis

Hoang Hai Bui

Spyridon Chapaloglou

Farid Khazaeli Moghadam

Junyong You

Yngve Heggelund

Henning Heiberg-Andersen

Martin Flügge

Mostafa Bakhoday Paskyabi

Trond Kvamsdal

Valentin Bruno Chabaud

Konstanze Kölle

Karl Otto Merz

Samarbeidspartnere

Universitetet i Bergen, NTNU, SINTEF Energi, Equinor Energy AS, WindSim AS, North China Electric Power University, Shanghai Jiao Tong University, Hohai University, North China University of Water Resources and Electric Power, Nanjing University of Science and Technology, China Three Gorges New Energy Co, Ltd, Goldwind Co. Ltd

Publications

Explainable transformer model for short-term wind forecast – You, Junyong; Heiberg-Andersen, Henning; Heggelund, Yngve; Stephansen, Annette. 2023, . NORCE.

Assimilation of marine atmospheric data in high-resolution wind forecasts – Heiberg-Andersen, Henning. 2023, . NORCE.

Stochastic analysis of a quasi-static reinforcement-learning approach for wake steering in dynamic wind farm simulations”, presented at EERA DeepWind, Trondheim – Chabaud, Valentin Bruno; Kölle, Konstanze; Chapaloglou, Spyridon; Yie, Jan. 2023, . SINTEFEN, NTNU, NCEPU.

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