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

Projektinformationen

ICONIC

ID Finanzhilfevereinbarung: 101122329

DOI

10.3030/101122329

EK-Unterschriftsdatum 30 August 2023

Startdatum 1 Dezember 2023

Enddatum 30 November 2027

Finanziert unter

• Climate, Energy and Mobility

Gesamtkosten

€ 3 897 447,51

EU-Beitrag

€ 3 897 447,50

Koordiniert durch

UNIVERSITEIT GENT
Belgium

CORDIS bietet Links zu öffentlichen Ergebnissen und Veröffentlichungen von HORIZONT-Projekten.

Links zu Ergebnissen und Veröffentlichungen von RP7-Projekten sowie Links zu einigen Typen spezifischer Ergebnisse wie Datensätzen und Software werden dynamisch von OpenAIRE abgerufen.

Leistungen

Documents, reports (7)

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.

High-fidelity models of WT and its critical components

High-fidelity models of WT and its critical components. It corresponds to T4.2: High-fidelity models to predict the response of each wind turbine and each critical component to external wind/control loads.

A control-oriented 3D dynamic WF wake model via CFD and deep learning with onshore & offshore case studies

A control-oriented 3D dynamic WF wake model via CFD and deep learning with onshore & offshore case studies. It corresponds to T2.1 with the same title.

Wind tunnel validation results of wind farm DT

Wind tunnel validation results of wind farm DT. It corresponds to T5.2 Validation of controls via wind tunnel experiments.

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.

Data sets, microdata, etc (1)

Flow field measurement dataset used for DT validations from wind tunnel experiments

Flow field measurement dataset used for DT validations from wind tunnel experiments. It corresponds to T5.1: Validation of the wind farm flow DT via wind tunnel experiments.

Veröffentlichungen

Peer reviewed articles (3)

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

Autoren: Jun Ye; Xiaowei Zhao; Yougang Bian; Manjiang Hu; Hongyang Dong
Veröffentlicht in: Nonlinear Dynamics, 2025, ISSN 1573-269X
Herausgeber: 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

Autoren: Felix C. Mehlan, Amir R. Nejad
Veröffentlicht in: Wind Energy Science, Ausgabe 10, 2025, ISSN 2366-7451
Herausgeber: Copernicus GmbH
DOI: 10.5194/WES-10-417-2025

Wind Farm Control via Offline Reinforcement Learning With Adversarial Training

Autoren: Yubo Huang, Xiaowei Zhao
Veröffentlicht in: IEEE Transactions on Automation Science and Engineering, Ausgabe 22, 2025, ISSN 1545-5955
Herausgeber: Institute of Electrical and Electronics Engineers (IEEE)
DOI: 10.1109/TASE.2025.3548565

Conference proceedings (5)

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

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

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

Autoren: Jincheng Zhang, Xiaowei Zhao
Veröffentlicht in: Journal of Physics: Conference Series, Ausgabe 2767, 2024, ISSN 1742-6588
Herausgeber: IOP Publishing
DOI: 10.1088/1742-6596/2767/9/092017

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

Autoren: Sterckx, I., Gryllias, K., Naets, F.
Veröffentlicht in: Advanced structural dynamics course, 2025
Herausgeber: Technical University of Denmark (DTU)

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

Autoren: Jalal Heidari, Nezmin Kayedpour, Lieven Vandevelde, Guillaume Crevecoeur
Veröffentlicht in: Journal of Physics: Conference Series, Ausgabe 3025, 2025, ISSN 1742-6588
Herausgeber: IOP Publishing
DOI: 10.1088/1742-6596/3025/1/012008

Stochastic MPC with Focus on Probabilistic Constraints with Application to Wind Turbine Control

Autoren: Torben Knudsen and Sina Hassani and Rafal Wisniewski
Herausgeber: 2025 American Control Conference (ACC)