Impact of the wake deficit model on wind farm yield: A study of yaw-based control optimization

2022 ◽  
Vol 220 ◽  
pp. 104827
Author(s):  
Bartłomiej P. Rak ◽  
R.B. Santos Pereira
Keyword(s):  
Wind Farm ◽  
Deficit Model ◽  
Control Optimization

scholarly journals Integrated wind farm layout and control optimization

2020 ◽  
Author(s):  
Mads M. Pedersen ◽  
Gunner C. Larsen
Keyword(s):  
Wind Speed ◽  
Wind Turbine ◽  
Wind Farm ◽  
Design Space ◽  
Wind Farm Layout ◽  
Control Optimization ◽  
Mean Wind Speed ◽  
The Individual ◽  
And Control ◽  
Selection Of

Abstract. Design of an optimal wind farm topology and wind farm control scheduling depends on the chosen metric. The objective of this paper is to investigate the influence of optimal wind farm control on the optimal wind farm layout in terms of power production. A successful fulfilment of this goal requires: 1) an accurate and fast flow model; 2) selection of the minimum set of design parameters that rules the problem; and 3) selection of an optimization algorithm with good scaling properties. For control of the individual wind farm turbines, the two most obvious strategies are wake steering based on active wind turbine yaw control and wind turbine derating. The present investigation is a priori limited to wind turbine derating. A high-speed linearized CFD RANS solver models the flow field and the crucial wind turbine wake interactions inside the wind farm. The actuator disk method is used to model the wind turbines, and utilizing an aerodynamic model, the design space of the optimization problem is reduced to only three variables per turbine – two geometric and one carefully selected variable specifying the individual wind turbine derating setting for each mean wind speed and direction. The full design space spanned by these (2N + Nd Ns N) parameters, where N is the number of wind farm turbines, Nd is the number of direction bins, and Ns is the number of mean wind speed bins. This design space is decomposed in two subsets, which in turn define a nested set of optimization problems to achieve the fastest possible optimization procedure. Following a simplistic sanity check of the platform functionality regarding wind farm layout and control optimization, the capabilities of the developed optimization platform is demonstrated on the Swedish offshore wind farm. For this particular wind farm, the analysis demonstrates that the expected annual energy production can be increased by 4 % by integrating the wind farm control in the design of the wind farm layout, which is 1.2 % higher than what is achieved by optimizing the layout only.

scholarly journals Integrated wind farm layout and control optimization

2020 ◽  
Vol 5 (4) ◽  
pp. 1551-1566
Author(s):  
Mads M. Pedersen ◽  
Gunner C. Larsen
Keyword(s):  
Wind Turbine ◽  
Wind Farm ◽  
Design Space ◽  
Wind Farm Layout ◽  
Control Optimization ◽  
Mean Wind Speed ◽  
The Individual ◽  
Full Design ◽  
And Control ◽  
Selection Of

Abstract. The objective of this paper is to investigate the joint optimization of wind farm layout and wind farm control in terms of power production. A successful fulfilment of this goal requires the following: (1) an accurate and fast flow model, (2) selection of the minimum set of design parameters that rules or governs the problem, and (3) selection of an optimization algorithm with good scaling properties. For control of the individual wind farm turbines with the aim of wind farm production optimization, the two most obvious strategies are wake steering based on active wind turbine yaw control and wind turbine derating. The present investigation is limited to wind turbine derating. A high-speed linearized computational fluid dynamics (CFD) Reynolds-averaged Navier–Stokes (RANS) solver models the flow field and the crucial wind turbine wake interactions inside the wind farm. The actuator disc method is used to model the wind turbines, and utilizing an aerodynamic model, the design space of the optimization problem is reduced to only three variables per turbine – two geometric and one carefully selected variable specifying the individual wind turbine derating setting for each mean wind speed and direction. The full design space is spanned by these (2N+NdNsN) parameters, where N is the number of wind farm turbines, Nd is the number of direction bins, and Ns is the number of mean wind speed bins. This design space is decomposed into two subsets, which in turn define a nested set of optimization problems to achieve a significantly faster optimization procedure compared to a direct optimization based on the full design space. Following a simplistic sanity check of the platform functionality regarding wind farm layout and control optimization, the capability of the developed optimization platform is demonstrated on a Swedish offshore wind farm. For this particular wind farm, the analysis demonstrates that the expected annual energy production can be increased by 4 % by integrating the wind farm control into the design of the wind farm layout, which is 1.2 % higher than what is achieved by optimizing the layout only.

scholarly journals Field Test of Wake Steering at an Offshore Wind Farm

2017 ◽  
Author(s):  
Paul Fleming ◽  
Jennifer Annoni ◽  
Jigar J. Shah ◽  
Linpeng Wang ◽  
Shreyas Ananthan ◽  
...  
Keyword(s):  
Field Test ◽  
Wind Farm ◽  
Offshore Wind ◽  
Engineering Model ◽  
Offshore Wind Farm ◽  
Steering Control ◽  
Dynamics Model ◽  
Yaw Control ◽  
Control Optimization ◽  
Smart Energy Management

Abstract. In this paper, a field test of wake steering control is presented. The field test is the result of a collaboration between the National Renewable Energy Laboratory (NREL) and Envision Energy, a smart energy management company and turbine manufacturer. In the campaign, an array of turbines within an operating commercial offshore wind farm in China have the normal yaw controller modified to implement wake steering according to a yaw control strategy. The strategy was designed using NREL wind farm models, including a computational fluid dynamics model, SOWFA, for understanding wake dynamics and an engineering model, FLORIS, for yaw control optimization. Results indicate that, within the certainty afforded by the data, the wake-steering controller was successful in increasing power capture, by amounts similar to those predicted from the models.

scholarly journals Evaluation of the impact of active wake control techniques on ultimate loads for a 10 MW wind turbine

2022 ◽  
Vol 7 (1) ◽  
pp. 1-17
Author(s):  
Alessandro Croce ◽  
Stefano Cacciola ◽  
Luca Sartori
Keyword(s):  
Wind Turbine ◽  
Energy Production ◽  
Wind Farm ◽  
Control Techniques ◽  
Control Optimization ◽  
Cost Of Energy ◽  
The Individual ◽  
The Cost ◽  
Wind Statistics ◽  
The Impact

Abstract. Wind farm control is one of the solutions recently proposed to increase the overall energy production of a wind power plant. A generic wind farm control is typically synthesized so as to optimize the energy production of the entire wind farm by reducing the detrimental effects due to wake–turbine interactions. As a matter of fact, the performance of a farm control is typically measured by looking at the increase in the power production, properly weighted through the wind statistics. Sometimes, fatigue loads are also considered in the control optimization problem. However, an aspect which is rather overlooked in the literature on this subject is the evaluation of the impact that a farm control law has on the individual wind turbine in terms of maximum loads and dynamic response under extreme conditions. In this work, two promising wind farm controls, based on wake redirection (WR) and dynamic induction control (DIC) strategy, are evaluated at the level of a single front-row wind turbine. To do so, a two-pronged analysis is performed. Firstly, the control techniques are evaluated in terms of the related impact on some specific key performance indicators, with special emphasis on ultimate loads and maximum blade deflection. Secondarily, an optimal blade redesign process is performed with the goal of quantifying the modification in the structure of the blade entailed by a possible increase in ultimate values due to the presence of wind farm control. Such an analysis provides for an important piece of information for assessing the impact of the farm control on the cost-of-energy model.

RESEARCH ON OVER VOLTAGE RIDE THROUGH ADAPTABILITY AND CONTROL OPTIMIZATION OF WIND FARM

2021 ◽  
Author(s):  
K. Yang ◽  
Z. Liu ◽  
P. Zhang ◽  
C. Liu ◽  
H. Liu
Keyword(s):  
Wind Farm ◽  
Control Optimization ◽  
And Control

scholarly journals Field test of wake steering at an offshore wind farm

2017 ◽  
Vol 2 (1) ◽  
pp. 229-239 ◽  
Author(s):  
Paul Fleming ◽  
Jennifer Annoni ◽  
Jigar J. Shah ◽  
Linpeng Wang ◽  
Shreyas Ananthan ◽  
...  
Keyword(s):  
Field Test ◽  
Wind Farm ◽  
Offshore Wind ◽  
Offshore Wind Farm ◽  
Steering Control ◽  
Dynamics Model ◽  
Yaw Control ◽  
Control Optimization ◽  
Model Flow ◽  
Smart Energy Management

Abstract. In this paper, a field test of wake-steering control is presented. The field test is the result of a collaboration between the National Renewable Energy Laboratory (NREL) and Envision Energy, a smart energy management company and turbine manufacturer. In the campaign, an array of turbines within an operating commercial offshore wind farm in China have the normal yaw controller modified to implement wake steering according to a yaw control strategy. The strategy was designed using NREL wind farm models, including a computational fluid dynamics model, Simulator fOr Wind Farm Applications (SOWFA), for understanding wake dynamics and an engineering model, FLOw Redirection and Induction in Steady State (FLORIS), for yaw control optimization. Results indicate that, within the certainty afforded by the data, the wake-steering controller was successful in increasing power capture, by amounts similar to those predicted from the models.

scholarly journals Wind Turbine Yaw Control Optimization and Its Impact on Performance

Machines ◽  
2019 ◽  
Vol 7 (2) ◽  
pp. 41 ◽  
Author(s):  
Davide Astolfi ◽  
Francesco Castellani ◽  
Francesco Natili
Keyword(s):  
Wind Turbine ◽  
Wind Turbines ◽  
Wind Farm ◽  
Principal Component Regression ◽  
Principal Component ◽  
Energy Conversion Efficiency ◽  
Test Case ◽  
Yaw Control ◽  
Control Optimization ◽  
Technology Improvement

The optimization of wind energy conversion efficiency has been recently boosting the technology improvement and the scientific comprehension of wind turbines. In this context, the yawing behavior of wind turbines has become a key topic: the yaw control can actually be exploited for optimization at the level of single wind turbine and of wind farm (for example, through active control of wakes). On these grounds, this work is devoted to the study of the yaw control optimization on a 2 MW wind turbine. The upgrade is estimated by analysing the difference between the measured post-upgrade power and a data driven model of the power according to the pre-upgrade behavior. Particular attention has therefore been devoted to the formulation of a reliable model for the pre-upgrade power of the wind turbine of interest, as a function of the operation variables of all the nearby wind turbines in the wind farm: the high correlation between the possible covariates of the model indicates that Principal Component Regression (PCR) is an adequate choice. Using this method, the obtained result for the selected test case is that the yaw control optimization provides a 1% of annual energy production improvement. This result indicates that wind turbine control optimization can non-negligibly improve the efficiency of wind turbine technology.

Evaluating Community Engagement and Benefit-Sharing Practices in Australian Wind Farm Development

2017 ◽  
Vol 1 (1) ◽  
pp. 1-6 ◽  
Author(s):  
Nina Lansbury Hall ◽  
Jarra Hicks ◽  
Taryn Lane ◽  
Emily Wood
Keyword(s):  
Community Engagement ◽  
Community Involvement ◽  
Wind Farm ◽  
Social Acceptance ◽  
Wind Farms ◽  
Clean Energy ◽  
Benefit Sharing ◽  
Delphi Panel ◽  
Government Organizations

The wind industry is positioned to contribute significantly to a clean energy future, yet the level of community opposition has at times led to unviable projects. Social acceptance is crucial and can be improved in part through better practice community engagement and benefit-sharing. This case study provides a “snapshot” of current community engagement and benefit-sharing practices for Australian wind farms, with a particular emphasis on practices found to be enhancing positive social outcomes in communities. Five methods were used to gather views on effective engagement and benefit-sharing: a literature review, interviews and a survey of the wind industry, a Delphi panel, and a review of community engagement plans. The overarching finding was that each community engagement and benefit-sharing initiative should be tailored to a community’s context, needs and expectations as informed by community involvement. This requires moving away from a “one size fits all” approach. This case study is relevant to wind developers, energy regulators, local communities and renewable energy-focused non-government organizations. It is applicable beyond Australia to all contexts where wind farm development has encountered conflicted societal acceptance responses.

Sign in / Sign up

Export Citation Format

Share Document