Smart, Aware, Integrated Wind Farm Control Interacting with Digital Twins (ICONIC)

Project Information

ICONIC

Grant agreement ID: 101122329

DOI

10.3030/101122329

EC signature date 30 August 2023

Start date 1 December 2023

End date 30 November 2027

Funded under

• Climate, Energy and Mobility

Total cost

€ 3 897 447,51

EU contribution

€ 3 897 447,50

Coordinated by

UNIVERSITEIT GENT
Belgium

CORDIS provides links to public deliverables and publications of HORIZON projects.

Links to deliverables and publications from FP7 projects, as well as links to some specific result types such as dataset and software, are dynamically retrieved from OpenAIRE .

Deliverables

Documents, reports (4)

Physics-based, data-augmented model for WF control design

Physics-based, data-augmented model for WF control design. It corresponds to T3.1 Physics-based, data-augmented model for wind turbine control design.

A multi-agent multi-layer DRL-based WF control framework

A multi-agent multi-layer DRL-based WF control framework. It corresponds to T2.2: A multi-agent, multi-layer, robust DRL framework to maximise farm-wide power production.

ICONIC project identity, website, D&E&C plan

ICONIC project identity, website, D&E&C plan. It corresponds to T6.1. Stakeholder involvement, clustering and liaising activities, T6.2. Dissemination of innovation and research results, T6.3. Management and exploitation of innovation results and T6.4. Communication and outreach activities.

Stochastic and Tube MPCs for turbine control

Stochastic and Tube MPCs for turbine control. It corresponds to T3.2: Stochastic and tube MPCs for turbine control with load reduction.

Publications

Peer reviewed articles (3)

Optimal control under safety constraints and disturbances: a multi-step, off-policy adaptive dynamic programming approach

Author(s): Jun Ye; Xiaowei Zhao; Yougang Bian; Manjiang Hu; Hongyang Dong
Published in: Nonlinear Dynamics, 2025, ISSN 1573-269X
Publisher: Springer
DOI: 10.1007/S11071-025-11329-3

On the modeling errors of digital twins for load monitoring and fatigue assessment in wind turbine drivetrains

Author(s): Felix C. Mehlan, Amir R. Nejad
Published in: Wind Energy Science, Issue 10, 2025, ISSN 2366-7451
Publisher: Copernicus GmbH
DOI: 10.5194/WES-10-417-2025

Wind Farm Control via Offline Reinforcement Learning With Adversarial Training

Author(s): Yubo Huang, Xiaowei Zhao
Published in: IEEE Transactions on Automation Science and Engineering, Issue 22, 2025, ISSN 1545-5955
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
DOI: 10.1109/TASE.2025.3548565

Conference proceedings (4)

Reference Model-Based Cyber-Attack Detection for Stochastically Uncertain Wind Turbine Systems

Author(s): Álvaro Martín Gómez, Sina Hassani, Szymon Gres, Rafal Wisniewski
Published in: 2025 IEEE Industry Applications Society Annual Meeting (IAS), 2025
Publisher: IEEE
DOI: 10.1109/IAS62731.2025.11061699

Reconstruction of dynamic wind turbine wake flow fields from virtual Lidar measurements via physics-informed neural networks

Author(s): Jincheng Zhang, Xiaowei Zhao
Published in: Journal of Physics: Conference Series, Issue 2767, 2024, ISSN 1742-6588
Publisher: IOP Publishing
DOI: 10.1088/1742-6596/2767/9/092017

Input torque estimation for wind turbine gearbox remaining useful life prediction​

Author(s): Sterckx, I., Gryllias, K., Naets, F.
Published in: Advanced structural dynamics course, 2025
Publisher: Technical University of Denmark (DTU)

Physics-Based, Data-Augmented Model for Wind Turbine Control Design

Author(s): Jalal Heidari, Nezmin Kayedpour, Lieven Vandevelde, Guillaume Crevecoeur
Published in: Journal of Physics: Conference Series, Issue 3025, 2025, ISSN 1742-6588
Publisher: IOP Publishing
DOI: 10.1088/1742-6596/3025/1/012008