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Wind Farm Control
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Wind Farm Control

A special issue of Energies (ISSN 1996-1073). This special issue belongs to the section "A3: Wind, Wave and Tidal Energy".

Deadline for manuscript submissions: closed (30 September 2021) | Viewed by 15467

Special Issue Editor

Dr. Stoyan Kanev

E-Mail Website
Guest Editor
TNO Energy Transition, Wind Energy group, Westerduinweg 3, 1755LE Petten, The Netherlands
Interests: wind farm control; wind turbine control

Special Issue Information

Dear Colleagues,

I am very happy to invite you to submit papers to a Special Issue of the journal Energies on “Wind Farm Control”.
The topic of wind farm control (WFC) relates to a wide range of methods and strategies to operate the wind turbines within a wind farm in a coordinated way to achieve a common target. WFC targets (and therefore topics for this Special Issue) could include:

  1. Reducing wake losses in order to increase the power production of the wind farm;
  2. Mitigating structural loads on the turbines’ components in order to reduce maintenance costs or increase lifetime;
  3. Ensuring that the active and reactive energy production comply with the infrastructural and contractual limitations;
  4. Matching the energy demand at minimum cost; or
  5. Supporting the grid stability by means of active and reactive power control.

While in the short term the focus lies on dealing with the practical implications related to the implementation of active wake control to boost the power production of wind farms (dynamic control, robustness to model and measurement uncertainty, turbine availability), in the long term WFC challenges relate to the implementation of a high share of wind into the energy system. Wind farms will need to be flexible enough to provide active and reactive power control to secure revenues in volatile markets and support grid operation. Furthermore, the integration of wind with other renewable sources, energy conversion systems, and energy storage systems will be needed in order to meet the demand variations and to comply with electricity transport limitations. Therefore, contributions proposing innovative controllers for hybrid renewable energy systems including energy generation, storage, and conversion are also acceptable for this Special Issue.
Four of the accepted papers will be nominated for free-of-charge processing in the special issue.

Looking forward to your contributions.

Dr. Stoyan Kanev
Guest Editor

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Energies is an international peer-reviewed open access semimonthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2600 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Keywords

  • wind farm control 
  • active wake control 
  • induction control
  • wake redirection 
  • wake mitigation
  • fatigue and ultimate loads
  • active power control
  • reactive power control
  • grid support
  • hybrid renewable energy systems (including wind)

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Published Papers (4 papers)

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Research

21 pages, 4346 KiB  
Article
Sensitivity and Uncertainty of the FLORIS Model Applied on the Lillgrund Wind Farm
by Maarten T. van Beek, Axelle Viré and Søren J. Andersen
Energies 2021, 14(5), 1293; https://doi.org/10.3390/en14051293 - 26 Feb 2021
Cited by 20 | Viewed by 4073
Abstract
Wind farms experience significant efficiency losses due to the aerodynamic interaction between turbines. A possible control technique to minimize these losses is yaw-based wake steering. This paper investigates the potential for improved performance of the Lillgrund wind farm through a detailed calibration of [...] Read more.
Wind farms experience significant efficiency losses due to the aerodynamic interaction between turbines. A possible control technique to minimize these losses is yaw-based wake steering. This paper investigates the potential for improved performance of the Lillgrund wind farm through a detailed calibration of a low-fidelity engineering model aimed specifically at yaw-based wake steering. The importance of each model parameter is assessed through a sensitivity analysis. This work shows that the model is overparameterized as at least one model parameter can be excluded from the calibration. The performance of the calibrated model is tested through an uncertainty analysis, which showed that the model has a significant bias but low uncertainty when comparing the predicted wake losses with measured wake losses. The model is used to optimize the annual energy production of the Lillgrund wind farm by determining yaw angles for specific inflow conditions. A significant energy gain is found when the optimal yaw angles are calculated deterministically. However, the energy gain decreases drastically when uncertainty in input conditions is included. More robust yaw angles can be obtained when the input uncertainty is taken into account during the optimization, which yields an energy gain of approximately 3.4%. Full article
(This article belongs to the Special Issue Wind Farm Control)
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20 pages, 1043 KiB  
Article
Influence of Wake Model Superposition and Secondary Steering on Model-Based Wake Steering Control with SCADA Data Assimilation
by Michael F. Howland and John O. Dabiri
Energies 2021, 14(1), 52; https://doi.org/10.3390/en14010052 - 24 Dec 2020
Cited by 13 | Viewed by 3326
Abstract
Methods for wind farm power optimization through the use of wake steering often rely on engineering wake models due to the computational complexity associated with resolving wind farm dynamics numerically. Within the transient, turbulent atmospheric boundary layer, closed-loop control is required to dynamically [...] Read more.
Methods for wind farm power optimization through the use of wake steering often rely on engineering wake models due to the computational complexity associated with resolving wind farm dynamics numerically. Within the transient, turbulent atmospheric boundary layer, closed-loop control is required to dynamically adjust to evolving wind conditions, wherein the optimal wake model parameters are estimated as a function of time in a hybrid physics- and data-driven approach using supervisory control and data acquisition (SCADA) data. Analytic wake models rely on wake velocity deficit superposition methods to generalize the individual wake deficit to collective wind farm flow. In this study, the impact of the wake model superposition methodologies on closed-loop control are tested in large eddy simulations of the conventionally neutral atmospheric boundary layer with full Coriolis effects. A model for the non-vanishing lateral velocity trailing a yaw misaligned turbine, termed secondary steering, is also presented, validated, and tested in the closed-loop control framework. Modified linear and momentum conserving wake superposition methodologies increase the power production in closed-loop wake steering control statistically significantly more than linear superposition. While the secondary steering model increases the power production and reduces the predictive error associated with the wake model, the impact is not statistically significant. Modified linear and momentum conserving superposition using the proposed secondary steering model increase a six turbine array power production, compared to baseline control, in large eddy simulations by 7.5% and 7.7%, respectively, with wake model predictive mean absolute errors of 0.03P1 and 0.04P1, respectively, where P1 is the baseline power production of the leading turbine in the array. Ensemble Kalman filter parameter estimation significantly reduces the wake model predictive error for all wake deficit superposition and secondary steering cases compared to predefined model parameters. Full article
(This article belongs to the Special Issue Wind Farm Control)
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21 pages, 1774 KiB  
Article
On the Potential of Reduced Order Models for Wind Farm Control: A Koopman Dynamic Mode Decomposition Approach
by Nassir Cassamo and Jan-Willem van Wingerden
Energies 2020, 13(24), 6513; https://doi.org/10.3390/en13246513 - 10 Dec 2020
Cited by 30 | Viewed by 3854
Abstract
The high dimensions and governing non-linear dynamics in wind farm systems make the design of numerical optimal controllers computationally expensive. A possible pathway to circumvent this challenge lies in finding reduced order models which can accurately embed the existing non-linearities. The work presented [...] Read more.
The high dimensions and governing non-linear dynamics in wind farm systems make the design of numerical optimal controllers computationally expensive. A possible pathway to circumvent this challenge lies in finding reduced order models which can accurately embed the existing non-linearities. The work presented here applies the ideas motivated by non-linear dynamical systems theory—the Koopman Operator—to an innovative algorithm in the context of wind farm systems—Input Output Dynamic Mode Decomposition (IODMD)—to improve on the ability to model the aerodynamic interaction between wind turbines in a wind farm and uncover insights into the existing dynamics. It is shown that a reduced order linear state space model can reproduce the downstream turbine generator power dynamics and reconstruct the upstream turbine wake. It is further shown that the fit can be improved by judiciously choosing the Koopman observables used in the IODMD algorithm without jeopardizing the models ability to rebuild the turbine wake. The extensions to the IODMD algorithm provide an important step towards the design of linear reduced order models which can accurately reproduce the dynamics in a wind farm. Full article
(This article belongs to the Special Issue Wind Farm Control)
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15 pages, 526 KiB  
Article
Wind Farm Loads under Wake Redirection Control
by Stoyan Kanev, Edwin Bot and Jack Giles
Energies 2020, 13(16), 4088; https://doi.org/10.3390/en13164088 - 6 Aug 2020
Cited by 22 | Viewed by 3435
Abstract
Active wake control (AWC) is a strategy for operating wind farms in such a way as to reduce the wake effects on the wind turbines, potentially increasing the overall power production. There are two concepts to AWC: induction control and wake redirection. The [...] Read more.
Active wake control (AWC) is a strategy for operating wind farms in such a way as to reduce the wake effects on the wind turbines, potentially increasing the overall power production. There are two concepts to AWC: induction control and wake redirection. The former strategy boils down to down-regulating the upstream turbines in order to increase the wind speed in their wakes. This has generally a positive effect on the turbine loading. The wake redirection concept, which relies on intentional yaw misalignment to move wakes away from downstream turbines, has a much more prominent impact and may lead to increased loading. Moreover, the turbines are typically not designed and certified to operate at large yaw misalignments. Even though the potential upsides in terms of power gain are very interesting, the risk for damage or downtime due to increased loading is seen as the main obstacle preventing large scale implementation of this technology. In order to provide good understanding on the impacts of AWC on the turbine loads, this paper presents the results from an in-depth analysis of the fatigue loads on the turbines of an existing wind farm. Even though for some wind turbine components the fatigue loads do increase for some wind conditions under yaw misalignment, it is demonstrated that the wake-induced loading decreases even more so that the lifetime loads under AWC are generally lower. Full article
(This article belongs to the Special Issue Wind Farm Control)
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