T2FL: An Efficient Model for Wind Turbine Fatigue Damage Prediction for the Two-Turbine Case

Galinos, Christos; Kazda, Jonas; Lio, Wai Hou; Giebel, Gregor

doi:10.3390/en13061306

Cited by 3 publications

(2 citation statements)

References 23 publications

(28 reference statements)

Supporting

Mentioning

Contrasting

Order By: Relevance

“…For example, a model developed by Dimitrov [17] maps the wake properties into turbine loads, while a study by Mendez Reyes et al [18] developed a load surrogate model that describes the turbine fatigue loads in terms of changes in yaw and wind inflow conditions. A recent study by Galinos et al [19] considered different down-regulation power set-points in their load surrogate models.…”

Section: Introductionmentioning

confidence: 99%

Surrogate Models for Wind Turbine Electrical Power and Fatigue Loads in Wind Farm

2020

Self Cite

View full text Add to dashboard Cite

Fatigue damage of turbine components is typically computed by running a rain-flow counting algorithm on the load signals of the components. This process is not linear and time consuming, thus, it is non-trivial for an application of wind farm control design and optimisation. To compensate this limitation, this paper will develop and compare different types of surrogate models that can predict the short term damage equivalent loads and electrical power of wind turbines, with respect to various wind conditions and down regulation set-points, in a wind farm. More specifically, Linear Regression, Artificial Neural Network and Gaussian Process Regression are the types of the developed surrogate models in this work. The results showed that Gaussian Process Regression outperforms the other types of surrogate models and can effectively estimate the aforementioned target variables.

show abstract

Section: Introductionmentioning

confidence: 99%

Surrogate Models for Wind Turbine Electrical Power and Fatigue Loads in Wind Farm

2020

Self Cite

View full text Add to dashboard Cite

show abstract

“…Advanced control strategies for coordinative optimization of active power dispatch and WT fatigue load have been studied in recent years. [6][7][8][9][10] Some control strategies focused on optimizing fatigue load by controlling turbulence distribution of wind farms, which redirect the wakes of the upwind WTs to minimize their effects to the downwind WTs. 6,9,10 These methods are suitable for flat terrain wind farms with strong wake effect and not for mountain wind farms with weak wake effects.…”

mentioning

confidence: 99%

Data‐driven modeling for fatigue loads of large‐scale wind turbines under active power regulation

et al. 2020

View full text Add to dashboard Cite

Advanced control methods for coordinatively optimizing active power dispatch and fatigue load distribution of wind turbines (WTs) in mountain wind farms, require the fatigue load modeling that has not been fully studied. This study proposes a data‐driven modeling method for fatigue loads of large‐scale WT under active power regulation. Firstly, load simulations based on Monte Carlo approach are conducted to overcome the uncertainty of fatigue load caused by turbulent wind. Subsequently, the target components of WT are selected according to the fatigue load dataset distribution at various power setpoints and the sensitivity to active power. Specifically, the Jarque–Bera statistic is used to evaluate the distribution characteristics of datasets; the single‐value processing determines the valid value through the maximum probability of the Gaussian kernel density distribution of the dataset; the Morris screening is employed to select the valid value with high sensitivity to active power regulation. Afterwards, arbitrary polynomial chaos expansion and support vector regression are performed to establish the data model of fatigue loads, respectively. Finally, the proposed method is applied into a 1.5‐MW commercial WT model designed by the manufacturer through the Bladed software. The obtained data‐driven model establishes a basis of simultaneously optimizing active power dispatch and fatigue load distribution, making it possible to reduce energy cost in mountain wind farms.

show abstract

Wind farm flow control: prospects and challenges

Bottasso²,

et al. 2022

Self Cite

View full text Add to dashboard Cite

Abstract. Wind farm control has been a topic of research for more than two decades. It has been identified as a core component of grand challenges in wind energy science to support accelerated wind energy deployment and to transition to a clean and sustainable energy system for the 21st century. The prospect of collective control of wind turbines in an array, to increase energy extraction, reduce structural loads, improve the balance of systems, reduce operation and maintenance costs, etc. has inspired many researchers over the years to propose innovative ideas and solutions. However, practical demonstration and commercialization of some of the more advanced concepts has been limited by a wide range of challenges, which include the complex physics of turbulent flows in wind farms and the atmosphere, uncertainties related to predicting structural load and failure statistics, and the highly multi-disciplinary nature of the overall design optimization problem, among others. In the current work, we aim at providing a comprehensive overview of the state of the art and outstanding challenges, thus identifying the key research areas that could further enable commercial uptake and success of wind farm control solutions. To this end, we have structured the discussion on challenges and opportunities into four main areas: (1) insight in control flow physics, (2) algorithms and AI, (3) validation and industry implementation, and (4) integrating control with system design (co-design).

show abstract

T2FL: An Efficient Model for Wind Turbine Fatigue Damage Prediction for the Two-Turbine Case

Cited by 3 publications

References 23 publications

Surrogate Models for Wind Turbine Electrical Power and Fatigue Loads in Wind Farm

Surrogate Models for Wind Turbine Electrical Power and Fatigue Loads in Wind Farm

Data‐driven modeling for fatigue loads of large‐scale wind turbines under active power regulation

Wind farm flow control: prospects and challenges

Contact Info

Product

Resources

About