Windfarm Power Optimization Using Yaw Angle Control

Dar, Zamiyad; Kar, Koushik; Sahni, Onkar; Chow, Joe H.

doi:10.1109/tste.2016.2585883

Cited by 35 publications

(29 citation statements)

References 17 publications

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“…In conjunction with these wake models and wind farm data, control algorithms can be used to optimize yaw angles. Several studies have developed optimization-based control approaches to adjust to the uncertainties, using approaches such as Bayesian optimization [41], genetic algorithm [42], game theory [43,44], random search [45], sequential optimization [32], gradient descent [28,46], greedy control [47], particle swarm optimization [48], dynamic programming [49,50]. Most recently, Howland et al [37] developed an control scheme based on an empirical fitted analytical wake model [26,40].…”

Section: Introductionmentioning

confidence: 99%

Wind Farm Yaw Optimization via Random Search Algorithm

Kuo

Pan

et al. 2020

Energies

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One direction in optimizing wind farm production is reducing wake interactions from upstream turbines. This can be done by optimizing turbine layout as well as optimizing turbine yaw and pitch angles. In particular, wake steering by optimizing yaw angles of wind turbines in farms has received significant attention in recent years. One of the challenges in yaw optimization is developing fast optimization algorithms which can find good solutions in real-time. In this work, we developed a random search algorithm to optimize yaw angles. Optimization was performed on a layout of 39 turbines in a 2 km by 2 km domain. Algorithm specific parameters were tuned for highest solution quality and lowest computational cost. Testing showed that this algorithm can find near-optimal (<1% of best known solutions) solutions consistently over multiple runs, and that quality solutions can be found under 200 iterations. Empirical results show that as wind farm density increases, the potential for yaw optimization increases significantly, and that quality solutions are likely to be plentiful and not unique.

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Section: Introductionmentioning

confidence: 99%

Wind Farm Yaw Optimization via Random Search Algorithm

Kuo

Pan

et al. 2020

Energies

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“…Simulation and wind tunnel test results are shown for a four-turbine case. Dynamic programming is another algorithm also applied to wind farm models (see, e.g., [143], [144], [145]). The latter aims at optimizing the power production among the yaw angles employing an extended Jensen Park model.…”

Section: A Optimization-based Closed-loop Controlmentioning

confidence: 99%

A tutorial on control-oriented modeling and control of wind farms

Boersma

Doekemeijer

Gebraad

et al. 2017

2017 American Control Conference (ACC)

157

147

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Wind turbines are often sited together in wind farms as it is economically advantageous. However, the wake inevitably created by every turbine will lead to a time-varying interaction between the individual turbines. Common practice in industry has been to control turbines individually and ignore this interaction while optimizing the power and loads of the individual turbines. However, turbines that are in a wake experience reduced wind speed and increased turbulence, leading to a reduced energy extraction and increased dynamic mechanical loads on the turbine, respectively. Neglecting the dynamic interaction between turbines in control will therefore lead to suboptimal behaviour of the total wind farm. Therefore, wind farm control has been receiving an increasing amount of attention over the past years, with the focus on increasing the total power production and reducing the dynamic loading on the turbines. In this paper, wind farm control-oriented modeling and control concepts are explained. In addition, recent developments and literature are discussed and categorized. This paper can serve as a source of background information and provides many references regarding control-oriented modeling and control of wind farms.

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“…One example of wind power plant control is wake steering, in which upwind turbines-that are waking downstream turbines-implement an offset angle to the wind to cause a redirection of their wake to improve the power capture downstream (Bastankah and Porte-Agel 2016; Gebraad et al 2014;Dar et al 2016;van Dijk et al 2017;Fleming et al 2014Fleming et al , 2016Campagnolo et al 2016;Howland et al 2016). This method can improve overall power capture of existing wind power plants as well as to-be-installed farms.…”

Section: Innovations To Reduce Lcoementioning

confidence: 99%

IEA Wind TCP: Results of IEA Wind TCP Workshop on a Grand Vision for Wind Energy Technology

Dykes

Veers

Lantz

et al. 2019

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Research advanced erosion-resistant composite materials for use on the leading edge of wind turbine blades Research, development, and scale-up of thermoplastic resin systems to reduce wind turbine blade manufacturing cycle times, reduce blade costs, enable thermal welding of bond lines, increase recyclability, and enable advanced in-field blade repair techniques Research advanced thermoset materials that can be recovered and reused Research metals and other noncomposite materials for development of lightweight, high-strength, and high-stiffness turbine components Grand Challenge 2: Automation Develop comprehensive techno-economic models for automation and a virtual factory (or digital twin of a factory) to understand advantages and disadvantages of targeted automation for both wind turbine blade molding and finishing operations Identify and quantify all cost inputs for wind turbine component manufacturing (e.g., blades, towers) to identify production steps that would benefit from automation Develop automated production methods for the continuous manufacturing of one-piece wind turbine towers for on-site manufacturing Develop advanced design tools to optimize blade and tower design with respect to manufacturing with automation Research and develop automation technology able to locate complex wind turbine blade geometry in space to allow for effective automated finishing operations, such as sanding, drilling, and cutting Develop methods to automate the delivery, placement, and inspection of core material into blade skin molds during the composite laminate skin lay-up Integrate real-time inspection and quality control into automated production steps for wind turbine blades, towers, and other components Develop large-scale, low-cost, high-throughput, high-performance automated additive manufacturing technologies (e.g., 3D printing, automated fiber placement and tape layup, filament winding) for the production of towers, blade skins, blade spars, and other turbine components Develop embedded metrology and out-of-mold indexing technologies Grand Challenge 3: On-Site Manufacturing Develop robust composite materials for use in broad on-site environmental conditions Research the chemistry and processing of in-situ thermoplastic resin systems to enable thermally welded joints in an on-site manufacturing environment Develop targeted wind turbine blade finishing automation techniques designed to reduce the overall footprint of on-site finishing operations and minimize production facility floor space Research additive manufacturing to print wind turbine blade molds and tooling at on-site manufacturing locations IEA Wind TCP Task 11

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Windfarm Power Optimization Using Yaw Angle Control

Cited by 35 publications

References 17 publications

Wind Farm Yaw Optimization via Random Search Algorithm

Wind Farm Yaw Optimization via Random Search Algorithm

A tutorial on control-oriented modeling and control of wind farms

IEA Wind TCP: Results of IEA Wind TCP Workshop on a Grand Vision for Wind Energy Technology

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