Title: Analytics for asset Integrity Management of Wind farms
Period: 2021 - 23
Budget: 1.55 MEur (of which 328 kEur to TUD)
Role: Co-PI
Funding source: Research Council of Norway, grant id 312486
Partners: Univ. of Agder (NO, coordinator), NORCE (NO), TU Delft (NL), DNV (NO, as advisor)
Description: About 65 GW of onshore wind turbine installations in Europe will reach end-of-design-life by 2028. It is time for the operators to decide on one of the three end-of-life scenarios, namely, decommissioning, lifetime extension, or repowering. The last two options will increase the operating life and thus reduce lifecycle costs. These end-of-life decisions require careful consideration of the accumulated fatigue life of each turbine in a wind farm to minimize monetary risk for the wind farm operators. Today, this decision is primarily based on a single point assessment by the certification authority. AIMWind proposes a continuous evaluation of wind farm health based on big data analytics using multimodal data such as wind, operational data, weather, condition monitoring, and inspection logs across a wind farm. Conventional approaches to fatigue estimation are slow and inadequate to achieve these goals, especially in large wind farms. Such a continuous health assessment will facilitate not only accurate life predictions but also continuous improvement of wind turbine operations to ensure long life and high availability.
The project AIMWind will take a three-pronged approach.
- We will also extend the condition monitoring systems as existing systems today focus only on a selected set of components providing incomplete health information.
- We will develop big data analytics using a fusion of physics-based models and novel deep-learning techniques to adequately estimate the accumulated fatigue in real-time, which does not exist today. We will use NORCOWE wind measurements, reference wind farm data, and other open data sources to achieve this (more details in the proposal document).
- We develop health-aware control technologies to achieve the dual objectives of efficiency and long life.
Thus, AIMWind plans to build the essential knowledge to reliable and efficient wind farm operation and improved chances for lifetime extension and repowering.
For more information check the project web site and the project page at the Research Council of Norway.
Publications
Enabling Co-Innovation for a Successful Digital Transformation in Wind Energy Using a New Digital Ecosystem and a Fault Detection Case Study
Barber, Sarah,
Lima, Luiz Andre Moyses,
Sakagami, Yoshiaki,
Quick, Julian,
Latiffianti, Effi,
Liu, Yichao,
Ferrari, Riccardo M.G.,
Letzgus, Simon,
Zhang, Xujie,
and Hammer, Florian
Energies
2022
In the next decade, further digitalisation of the entire wind energy project lifecycle is expected to be a major driver for reducing project costs and risks. In this paper, a literature review on the challenges related to implementation of digitalisation in the wind energy industry is first carried out, showing that there is a strong need for new solutions that enable co-innovation within and between organisations. Therefore, a new collaboration method based on a digital ecosystem is developed and demonstrated. The method is centred around specific “challenges”, which are defined by “challenge providers” within a topical “space” and made available to participants via a digital platform. The data required in order to solve a particular “challenge” are provided by the “challenge providers” under the confidentiality conditions they specify. The method is demonstrated via a case study, the EDP Wind Turbine Fault Detection Challenge. Six submitted solutions using diverse approaches are evaluated. Two of the solutions perform significantly better than EDP’s existing solution in terms of Total Prediction Costs (saving up to €120,000). The digital ecosystem is found to be a promising solution for enabling co-innovation in wind energy in general, providing a number of tangible benefits for both challenge and solution providers.
Hybrid Design of Multiplicative Watermarking for Defense Against Malicious Parameter Identification
Zhang, Jiaxuan,
Gallo, Alexander J.,
and Ferrari, Riccardo M.G.
In Conference on Decision and Control
2023
An Economic Model Predictive Control Approach for Load Mitigation on Multiple Tower Locations of Wind Turbines
Feng, Zhixin,
Gallo, Alexander J.,
Liu, Yichao,
Pamososuryo, Atindriyo Kusumo,
Ferrari, Riccardo M.G.,
and Wingerden, Jan-Willem
In Conference on Decision and Control
2022
The current trend in the evolution of wind tur- bines is to increase their rotor size in order to capture more power. This leads to taller, slender and more flexible towers, which thus experience higher dynamical loads due to the tur- bine rotation and environmental factors. It is hence compelling to deploy advanced control methods that can dynamically counteract such loads, especially at tower positions that are more prone to develop cracks or corrosion damages. Still, to the best of the authors’ knowledge, little to no attention has been paid in the literature to load mitigation at multiple tower locations. Furthermore, there is a need for control schemes that can balance load reduction with optimization of power production. In this paper, we develop an Economic Model Predictive Control (eMPC) framework to address such needs. First, we develop a linear modal model to account for the tower flexural dynamics. Then we incorporate it into an eMPC framework, where the dynamics of the turbine rotation are expressed in energy terms. This allows us to obtain a convex formulation, that is computationally attractive. Our control law is designed to avoid the “turn-pike” behavior and guarantee recursive feasibility. We demonstrate the performance of the proposed controller on a 5MW reference WT model: the results illustrate that the proposed controller is able to reduce the tower loads at multiple locations, without significant effects to the generated power.
Hierarchical Cyber-Attack Detection in Large-Scale Interconnected Systems
Keijzer, Twan,
Gallo, Alexander J.,
and Ferrari, Riccardo M.G.
In Conference on Decision and Control
2022
In this paper we present a hierarchical scheme to detect cyber-attacks in a hierarchical control architecture for large-scale interconnected systems (LSS). We consider the LSS as a network of physically coupled subsystems, equipped with a two-layer controller: on the local level, decentralized controllers guarantee overall stability and reference tracking; on the supervisory level, a centralized coordinator sets references for the local regulators. We present a scheme to detect attacks that occur at the local level, with malicious agents capable of affecting the local control. The detection scheme is computed at the supervisory level, requiring only limited exchange of data and model knowledge. We offer detailed theoretical analysis of the proposed scheme, highlighting its detection properties in terms of robustness, detectability and stealthiness conditions.
Cryptographic switching functions for multiplicative watermarking in cyber-physical systems
Gallo, Alexander J.,
and Ferrari, Riccardo M.G.
In IFAC Symposium on Fault Detection, Supervision and Safety for Technical Processes
2022
In this paper we present a novel switching function for multiplicative watermarking systems. The switching function is based on the algebraic structure of elliptic curves over finite fields. The resulting function allows for both watermarking generator and remover to define appropriate system parameters, sharing only limited information, namely a private key. We prove that the resulting watermarking parameters lead to a stable watermarking scheme.
Design of multiplicative watermarking against covert attacks
Gallo, Alexander J.,
Anand, Sribalaji C.,
Teixeira, André M.H.,
and Ferrari, Riccardo M.G.
In Conference on Decision and Control
2021