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

Informazioni relative al progetto

ICONIC

ID dell’accordo di sovvenzione: 101122329

DOI

10.3030/101122329

Data della firma CE 30 Agosto 2023

Data di avvio 1 Dicembre 2023

Data di completamento 30 Novembre 2027

Finanziato da

• Climate, Energy and Mobility

Costo totale

€ 3 897 447,51

Contributo UE

€ 3 897 447,50

Coordinato da

UNIVERSITEIT GENT
Belgium

CORDIS fornisce collegamenti ai risultati finali pubblici e alle pubblicazioni dei progetti ORIZZONTE.

I link ai risultati e alle pubblicazioni dei progetti del 7° PQ, così come i link ad alcuni tipi di risultati specifici come dataset e software, sono recuperati dinamicamente da .OpenAIRE .

Risultati finali

Documents, reports (10)

Degradation models of critical components to assess their lifetime

Degradation models of critical components to assess their lifetime. It corresponds to T4.3: Degradation models to assess lifetime of critical components.

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 wind farm ancillary service algorithm with optimal fatigue distribution

A wind farm ancillary service algorithm with optimal fatigue distribution. It corresponds to T2.3: Ancillary service support with optimal fatigue distribution.

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 cybersecurity mechanism in wind farm control

A cybersecurity mechanism in wind farm control. It corresponds to T2.6: Solutions to ensure cybersecurity in wind farm control.

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.

A wind farm (WF) digital twin solution via physics-informed deep learning

A wind farm (WF) digital twin solution via physics-informed deep learning. It corresponds to T4.1: Wind farm flow digital twin via physics-informed deep learning.

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.

Pubblicazioni

Peer reviewed articles (3)

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

Autori: Jun Ye; Xiaowei Zhao; Yougang Bian; Manjiang Hu; Hongyang Dong
Pubblicato in: Nonlinear Dynamics, 2025, ISSN 1573-269X
Editore: 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

Autori: Felix C. Mehlan, Amir R. Nejad
Pubblicato in: Wind Energy Science, Numero 10, 2025, ISSN 2366-7451
Editore: Copernicus GmbH
DOI: 10.5194/WES-10-417-2025

Wind Farm Control via Offline Reinforcement Learning With Adversarial Training

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

Conference proceedings (6)

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

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

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

Autori: Jincheng Zhang, Xiaowei Zhao
Pubblicato in: Journal of Physics: Conference Series, Numero 2767, 2024, ISSN 1742-6588
Editore: IOP Publishing
DOI: 10.1088/1742-6596/2767/9/092017

Estimating Tower Vibrations with Data-Driven Models: Possibility of Optimizing Control Actions to Reduce Loads on Wind Turbine

Autori: Jalal Heidari, Nezmin Kayedpour, Lieven Vandevelde and Guillaume Crevecoeur
Editore: Wind Energy Science Conference (WESC) 2025

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

Autori: Sterckx, I., Gryllias, K., Naets, F.
Pubblicato in: Advanced structural dynamics course, 2025
Editore: Technical University of Denmark (DTU)

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

Autori: Jalal Heidari, Nezmin Kayedpour, Lieven Vandevelde, Guillaume Crevecoeur
Pubblicato in: Journal of Physics: Conference Series, Numero 3025, 2025, ISSN 1742-6588
Editore: IOP Publishing
DOI: 10.1088/1742-6596/3025/1/012008

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

Autori: Torben Knudsen and Sina Hassani and Rafal Wisniewski
Editore: 2025 American Control Conference (ACC)