Wind farm control ‐ Part I: A review on control system concepts and structures

Andersson, Leif Erik; Anaya‐Lara, Olimpo; Tande, John Olav Giæver; Merz, Karl; Imsland, Lars

doi:10.1049/rpg2.12160

Cited by 54 publications

(33 citation statements)

References 225 publications

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“…How the wakes are controlled at the plant level will therefore influence the drivetrain design life (van Binsbergen et al, 2020). Several approaches which have recently been proposed to reduce overall plant losses by reducing wake effects employ either some form of static or dynamic induction control or wake steering by yaw control (Andersson et al, 2021). Static induction control aims to reduce the strength of the wake of an upstream machine by changing the pitch of the blades or the rotational speed of the rotor in such a way as to reduce the thrust at the expense of some efficiency but to allow downstream machines to see an increased wind speed so that the combined output of the turbines is increased.…”

Section: Drivetrain and Plant Considerationmentioning

confidence: 99%

Wind turbine drivetrains: state-of-the-art technologies and future development trends

et al. 2022

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Abstract. This paper presents the state-of-the-art technologies and development trends of wind turbine drivetrains – the system that converts kinetic energy of the wind to electrical energy – in different stages of their life cycle: design, manufacturing, installation, operation, lifetime extension, decommissioning and recycling. Offshore development and digitalization are also a focal point in this study. Drivetrain in this context includes the whole power conversion system: main bearing, shafts, gearbox, generator and power converter. The main aim of this article is to review the drivetrain technology development as well as to identify future challenges and research gaps. The main challenges in drivetrain research identified in this paper include drivetrain dynamic responses in large or floating turbines, aerodynamic and farm control effects, use of rare-earth material in generators, improving reliability through prognostics, and use of advances in digitalization. These challenges illustrate the multidisciplinary aspect of wind turbine drivetrains, which emphasizes the need for more interdisciplinary research and collaboration.

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Section: Drivetrain and Plant Considerationmentioning

confidence: 99%

Wind turbine drivetrains: state-of-the-art technologies and future development trends

et al. 2022

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“…Fleming et al, 2017;Annoni et al, 2018;Doekemeijer et al, 2021;Bossanyi and Ruisi, 2021;Simley et al, 2021). A comprehensive review of the power maximisation through WFFC is presented in Kheirabadi and Nagamune (2019) and Andersson et al (2021). To realise those benefits, the control strategy might be 1) Axial induction control, in which some upstream turbines will lower their energy capture (also referred as curtailment, down-regulation or derating) hence increasing the wind velocity and reducing the turbulence downstream; and/or 2) Wake steering, in which some of the turbines will be misaligned to redirect the wake away from the other turbines hence mitigating the wake effects; and 3) Wake mixing where upstream turbines are dynamically up-regulated and down-regulated on short time scales to induce additional wake mixing and wake recovery, minimising the losses further downstream.…”

Section: Introductionmentioning

confidence: 99%

FarmConners Wind Farm Flow Control Benchmark: Blind Test Results

Göçmen

Campagnolo²,

Duc

et al. 2022

Preprint

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Abstract. Wind farm flow control (WFFC) is a topic of interest at several research institutes, industry and certification agencies world-wide. For reliable performance assessment of the technology, the efficiency and the capability of the models applied to WFFC should be carefully evaluated. To address that, FarmConners consortium has launched a common benchmark for code comparison under controlled operation to demonstrate its potential benefits such as increased power production. The benchmark builds on available data sets from previous field campaigns, wind tunnel experiments and high-fidelity simulations. Within that database, 4 blind tests are defined and 13 participants in total have submitted results for the analysis of single and multiple wake under WFFC. Some participants took part in several blind tests and some participants have implemented several models. The observations and/or the model outcomes are evaluated via direct power comparisons at the upstream and downstream turbine(s), as well as the power gain at the wind farm level under wake steering control strategy. Additionally, wake loss reduction is also analysed to support the power performance comparison, where relevant. Majority of the participating models show good agreement with the observations or the reference high-fidelity simulations, especially for lower degrees of upstream misalignment and narrow wake sector. However, the benchmark clearly highlights the importance of the calibration procedure for control-oriented models. The potential effects of limited controlled operation data in calibration is particularly visible via frequent model mismatch for highly deflected wakes, as well as the power loss at the controlled turbine(s). In addition to the flow modelling, sensitivity of the predicted WFFC benefits to the turbine representation and the implementation of the controller is also underlined. FarmConners benchmark is the first of its kind to bring a wide variety of data sets, control settings and model complexities for the (initial) assessment of farm flow control benefits. It forms an important basis for more detailed benchmarks in the future with extended control objectives to assess the true value of WFFC.

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“…Wind farm flow control (WFFC) is a holistic approach that utilises the control degrees of freedom of the individual wind turbines to improve the individual wake characteristics to benefit the entire wind farm. The two most prominent farm flow control strategies are axial induction control and wake redirection control [1]. Both strategies aim to reduce the wake deficit for the subsequent turbine and enhance mixing and wake recovery, and both strategies can be employed either statically or dynamically.…”

Section: Introductionmentioning

confidence: 99%

Probabilistic surrogates for flow control using combined control strategies

Debusscher

Göçmen

Andersen

2022

J. Phys.: Conf. Ser.

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Wind farm flow control (WFFC) is a promising technology for improving wind farm operation and design. The presented study focuses on the combination of the two most prominent WFFC strategies, yaw-based wake-steering and axial induction control via constant blade pitch, for maximising the wind farm power production with and without a load constraint. The optimisation is performed via data-driven polynomial-based probabilistic surrogate models, calibrated through a range of LES and aeroelastic simulations for a 2-turbine setup. The results indicate the yaw-based wake-steering to be the driving mechanism to increase the wind farm power production, particularly when loads are not considered. However, axial induction is seen beneficial for load alleviation, especially in close spacings. Overall, the analyses highlight the potential of combined WFFC strategies for power optimisation in a safety-critical system and provides a probabilistic approach for data-driven multi-objective farm flow control.

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Wind farm control ‐ Part I: A review on control system concepts and structures

Cited by 54 publications

References 225 publications

Wind turbine drivetrains: state-of-the-art technologies and future development trends

Wind turbine drivetrains: state-of-the-art technologies and future development trends

FarmConners Wind Farm Flow Control Benchmark: Blind Test Results

Probabilistic surrogates for flow control using combined control strategies

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