Research

25 pages, 1408 KiB

Open AccessArticle

Wind Turbine Performance in Very Large Wind Farms: Betz Analysis Revisited

by Jacob R. West and Sanjiva K. Lele

Energies 2020, 13(5), 1078; https://doi.org/10.3390/en13051078 - 01 Mar 2020

Cited by 9 | Viewed by 3894

The theoretical limit for wind turbine performance, the so-called Betz limit, arises from an inviscid, irrotational analysis of the streamtube around an actuator disk. In a wind farm in the atmospheric boundary layer, the physics are considerably more complex, encompassing shear, turbulent transport, [...] Read more.

The theoretical limit for wind turbine performance, the so-called Betz limit, arises from an inviscid, irrotational analysis of the streamtube around an actuator disk. In a wind farm in the atmospheric boundary layer, the physics are considerably more complex, encompassing shear, turbulent transport, and wakes from other turbines. In this study, the mean flow streamtube around a wind turbine in a wind farm is investigated with large eddy simulations of a periodic array of actuator disks in half-channel flow at a range of turbine thrust coefficients. Momentum and mean kinetic energy budgets are presented, connecting the energy budget for an individual turbine to the wind farm performance as a whole. It is noted that boundary layer turbulence plays a key role in wake recovery and energy conversion when considering the entire wind farm. The wind farm power coefficient is maximized when the work done by Reynolds stress on the periphery of the streamtube is maximized, although some mean kinetic energy is also dissipated into turbulence. This results in an optimal value of thrust coefficient lower than the traditional Betz result. The simulation results are used to evaluate Nishino’s model of infinite wind farms, and design trade-offs described by it are presented. Full article

(This article belongs to the Special Issue State of the Art of Wind Farm Optimization)

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15 pages, 604 KiB

Open AccessArticle

Wind Farm Yaw Optimization via Random Search Algorithm

by Jim Kuo, Kevin Pan, Ni Li and He Shen

Energies 2020, 13(4), 865; https://doi.org/10.3390/en13040865 - 16 Feb 2020

Cited by 16 | Viewed by 3783

Abstract

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 [...] Read more.

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. Full article

(This article belongs to the Special Issue State of the Art of Wind Farm Optimization)

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15 pages, 5021 KiB

Open AccessArticle

Simulated Annealing Algorithm for Wind Farm Layout Optimization: A Benchmark Study

by Kyoungboo Yang and Kyungho Cho

Energies 2019, 12(23), 4403; https://doi.org/10.3390/en12234403 - 20 Nov 2019

Cited by 21 | Viewed by 3336

Abstract

The optimal layout of wind turbines is an important factor in the wind farm design process, and various attempts have been made to derive optimal deployment results. For this purpose, many approaches to optimize the layout of turbines using various optimization algorithms have [...] Read more.

The optimal layout of wind turbines is an important factor in the wind farm design process, and various attempts have been made to derive optimal deployment results. For this purpose, many approaches to optimize the layout of turbines using various optimization algorithms have been developed and applied across various studies. Among these methods, the most widely used optimization approach is the genetic algorithm, but the genetic algorithm handles many independent variables and requires a large amount of computation time. A simulated annealing algorithm is also a representative optimization algorithm, and the simulation process is similar to the wind turbine layout process. However, despite its usefulness, it has not been widely applied to the wind farm layout optimization problem. In this study, a wind farm layout optimization method was developed based on simulated annealing, and the performance of the algorithm was evaluated by comparing it to those of previous studies under three wind scenarios; likewise, the applicability was examined. A regular layout and optimal number of wind turbines, never before observed in previous studies, were obtained and they demonstrated the best fitness values for all the three considered scenarios. The results indicate that the simulated annealing (SA) algorithm can be successfully applied to the wind farm layout optimization problem. Full article

(This article belongs to the Special Issue State of the Art of Wind Farm Optimization)

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Graphical abstract

19 pages, 3431 KiB

Open AccessArticle

A Wake Modeling Paradigm for Wind Farm Design and Control

by Carl R. Shapiro, Genevieve M. Starke, Charles Meneveau and Dennice F. Gayme

Energies 2019, 12(15), 2956; https://doi.org/10.3390/en12152956 - 01 Aug 2019

Cited by 54 | Viewed by 4550

Abstract

Wake models play an integral role in wind farm layout optimization and operations where associated design and control decisions are only as good as the underlying wake model upon which they are based. However, the desired model fidelity must be counterbalanced by the [...] Read more.

Wake models play an integral role in wind farm layout optimization and operations where associated design and control decisions are only as good as the underlying wake model upon which they are based. However, the desired model fidelity must be counterbalanced by the need for simplicity and computational efficiency. As a result, efficient engineering models that accurately capture the relevant physics—such as wake expansion and wake interactions for design problems and wake advection and turbulent fluctuations for control problems—are needed to advance the field of wind farm optimization. In this paper, we discuss a computationally-efficient continuous-time one-dimensional dynamic wake model that includes several features derived from fundamental physics, making it less ad-hoc than prevailing approaches. We first apply the steady-state solution of the model to predict the wake expansion coefficients commonly used in design problems. We demonstrate that more realistic results can be attained by linking the wake expansion rate to a top-down model of the atmospheric boundary layer, using a super-Gaussian wake profile that smoothly transitions between a top-hat and Gaussian distribution as well as linearly-superposing wake interactions. We then apply the dynamic model to predict trajectories of wind farm power output during start-up and highlight the improved accuracy of non-linear advection over linear advection. Finally, we apply the dynamic model to the control-oriented application of predicting power output of an irregularly-arranged farm during continuous operation. In this application, model fidelity is improved through state and parameter estimation accounting for spanwise inflow inhomogeneities and turbulent fluctuations. The proposed approach thus provides a modeling paradigm with the flexibility to enable designers to trade-off between accuracy and computational speed for a wide range of wind farm design and control applications. Full article

(This article belongs to the Special Issue State of the Art of Wind Farm Optimization)

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23 pages, 5924 KiB

Open AccessArticle

Aerodynamically Interacting Vertical-Axis Wind Turbines: Performance Enhancement and Three-Dimensional Flow

by Ian D. Brownstein, Nathaniel J. Wei and John O. Dabiri

Energies 2019, 12(14), 2724; https://doi.org/10.3390/en12142724 - 16 Jul 2019

Cited by 42 | Viewed by 5285

Abstract

This study examined three-dimensional, volumetric mean velocity fields and corresponding performance measurements for an isolated vertical-axis wind turbine (VAWT) and for co- and counter-rotating pairs of VAWTs with varying incident wind direction and turbine spacings. The purpose was to identify turbine configurations and [...] Read more.

This study examined three-dimensional, volumetric mean velocity fields and corresponding performance measurements for an isolated vertical-axis wind turbine (VAWT) and for co- and counter-rotating pairs of VAWTs with varying incident wind direction and turbine spacings. The purpose was to identify turbine configurations and flow mechanisms that can improve the power densities of VAWT arrays in wind farms. All experiments were conducted at a Reynolds number of

R e_{D} = 7.3 \times 10^{4}

. In the paired arrays, performance enhancement was observed for both the upstream and downstream turbines. Increases in downstream turbine performance correlate with bluff–body accelerations around the upstream turbine, which increase the incident freestream velocity on the downstream turbine in certain positions. Decreases in downstream turbine performance are determined by its position in the upstream turbine’s wake. Changes in upstream turbine performance are related to variations in the surrounding flow field due to the presence of the downstream rotor. For the most robust array configuration studied, an average 14% increase in array performance over approximately a 50° range of wind direction was observed. Additionally, three-dimensional vortex interactions behind pairs of VAWT were observed that can replenish momentum in the wake by advection rather than turbulent diffusion. These effects and their implications for wind-farm design are discussed. Full article

(This article belongs to the Special Issue State of the Art of Wind Farm Optimization)

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21 pages, 3739 KiB

Open AccessFeature PaperArticle

Wind Farm Modeling with Interpretable Physics-Informed Machine Learning

by Michael F. Howland and John O. Dabiri

Energies 2019, 12(14), 2716; https://doi.org/10.3390/en12142716 - 16 Jul 2019

Cited by 35 | Viewed by 5717

Abstract

Turbulent wakes trailing utility-scale wind turbines reduce the power production and efficiency of downstream turbines. Thorough understanding and modeling of these wakes is required to optimally design wind farms as well as control and predict their power production. While low-order, physics-based wake models [...] Read more.

Turbulent wakes trailing utility-scale wind turbines reduce the power production and efficiency of downstream turbines. Thorough understanding and modeling of these wakes is required to optimally design wind farms as well as control and predict their power production. While low-order, physics-based wake models are useful for qualitative physical understanding, they generally are unable to accurately predict the power production of utility-scale wind farms due to a large number of simplifying assumptions and neglected physics. In this study, we propose a suite of physics-informed statistical models to accurately predict the power production of arbitrary wind farm layouts. These models are trained and tested using five years of historical one-minute averaged operational data from the Summerview wind farm in Alberta, Canada. The trained models reduce the prediction error compared both to a physics-based wake model and a standard two-layer neural network. The trained parameters of the statistical models are visualized and interpreted in the context of the flow physics of turbulent wind turbine wakes. Full article

(This article belongs to the Special Issue State of the Art of Wind Farm Optimization)

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15 pages, 1289 KiB

Open AccessArticle

Implementation and Analyses of Yaw Based Coordinated Control of Wind Farms

by Tanvir Ahmad, Abdul Basit, Muneeb Ahsan, Olivier Coupiac, Nicolas Girard, Behzad Kazemtabrizi and Peter C. Matthews

Energies 2019, 12(7), 1266; https://doi.org/10.3390/en12071266 - 02 Apr 2019

Cited by 17 | Viewed by 3086

Abstract

This paper presents, with a live field experiment, the potential of increasing wind farm power generation by optimally yawing upstream wind turbine for reducing wake effects as a part of the SmartEOLE project. Two 2MW turbines from the Le Sole de Moulin Vieux [...] Read more.

This paper presents, with a live field experiment, the potential of increasing wind farm power generation by optimally yawing upstream wind turbine for reducing wake effects as a part of the SmartEOLE project. Two 2MW turbines from the Le Sole de Moulin Vieux (SMV) wind farm are used for this purpose. The upstream turbine (SMV6) is operated with a yaw offset (

α

) in a range of

- 12

° to 8° for analysing the impact on the downstream turbine (SMV5). Simulations are performed with intelligent control strategies for estimating optimum

α

settings. Simulations show that optimal

α

can increase net production of the two turbines by more than 5%. The impact of

α

on SMV6 is quantified using the data obtained during the experiment. A comparison of the data obtained during the experiment is carried out with data obtained during normal operations in similar wind conditions. This comparison show that an optimum or near-optimum

α

increases net production by more than 5% in wake affected wind conditions, which is in confirmation with the simulated results. Full article

(This article belongs to the Special Issue State of the Art of Wind Farm Optimization)

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17 pages, 2266 KiB

Open AccessArticle

Fast Processing Intelligent Wind Farm Controller for Production Maximisation

by Tanvir Ahmad, Abdul Basit, Juveria Anwar, Olivier Coupiac, Behzad Kazemtabrizi and Peter C. Matthews

Energies 2019, 12(3), 544; https://doi.org/10.3390/en12030544 - 10 Feb 2019

Cited by 14 | Viewed by 3561

Abstract

A practical wind farm controller for production maximisation based on coordinated control is presented. The farm controller emphasises computational efficiency without compromising accuracy. The controller combines particle swarm optimisation (PSO) with a turbulence intensity–based Jensen wake model (TI–JM) for exploiting the benefits of [...] Read more.

A practical wind farm controller for production maximisation based on coordinated control is presented. The farm controller emphasises computational efficiency without compromising accuracy. The controller combines particle swarm optimisation (PSO) with a turbulence intensity–based Jensen wake model (TI–JM) for exploiting the benefits of either curtailing upstream turbines using coefficient of power (

C_{P}

) or deflecting wakes by applying yaw-offsets for maximising net farm production. Firstly, TI–JM is evaluated using convention control benchmarking WindPRO and real time SCADA data from three operating wind farms. Then the optimised strategies are evaluated using simulations based on TI–JM and PSO. The innovative control strategies can optimise a medium size wind farm, Lillgrund consisting of 48 wind turbines, requiring less than 50 s for a single simulation, increasing farm efficiency up to a maximum of 6% in full wake conditions. Full article

(This article belongs to the Special Issue State of the Art of Wind Farm Optimization)

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