scholarly journals The Application of Water Cycle Optimization Algorithm for Optimal Placement of Wind Turbines in Wind Farms

Energies ◽  
2019 ◽  
Vol 12 (22) ◽  
pp. 4335 ◽  
Author(s):  
Hegazy Rezk ◽  
Ahmed Fathy ◽  
Ahmed A. Zaki Diab ◽  
Mujahed Al-Dhaifallah
Keyword(s):  
Wind Speed ◽  
Wind Turbines ◽  
Wind Direction ◽  
Wind Farm ◽  
Water Cycle ◽  
Reliability And Validity ◽  
Wind Farms ◽  
Optimal Placement ◽  
Grey Wolf Optimizer ◽  
Variable Wind

Wind farms (WFs) include an enormous number of wind turbines (WTs) in order to achieve high capacity. The interaction among WTs reduces the extracted amount of wind energy because wind speed decreases in the wake region. The optimal placement of WTs within a WF is therefore vital for achieving high performance. This permits as many WTs as possible to be installed inside a narrow region. In this work, the water cycle algorithm (WCA), a recently developed optimizer, was employed to identify the optimal distribution of WTs. Minimization of the total cost per kilowatt was the objective of the optimization process. Two different cases were considered: the first assumed constant wind speed with variable wind direction, while the second applied variable wind speed with variable wind direction. The results obtained through the WCA optimizer were compared with other algorithms, namely, salp swarm algorithm (SSA), satin bowerbird optimization (SBO), grey wolf optimizer (GWO), and differential evolution (DE), as well as other reported works. WCA gave the best solution compared to other reported and programmed algorithms, thus confirming the reliability and validity of WCA in optimally configuring turbines in a wind farm for both the studied cases.

Optimization of Large Wind Farms Using a Genetic Algorithm

2012 ◽  
Author(s):  
Anshul Mittal ◽  
Lafayette K. Taylor
Keyword(s):  
Genetic Algorithm ◽  
Wind Speed ◽  
Wind Turbines ◽  
Wind Direction ◽  
Wind Farm ◽  
Wind Farms ◽  
Unit Power ◽  
Wake Model ◽  
Variable Wind ◽  
Large Wind

Optimizing the placement of the wind turbines in a wind farm to achieve optimal performance is an active area of research, with numerous research studies being published every year. Typically, the area available for the wind farm is divided into cells (a cell may/may not contain a wind turbine) and an optimization algorithm is used. In this study, the effect of the cell size on the optimal layout is being investigated by reducing it from five rotor diameter (previous studies) to 1/40 rotor diameter (present study). A code is developed for optimizing the placement of wind turbines in large wind farms. The objective is to minimize the cost per unit power produced from the wind farm. A genetic algorithm is employed for the optimization. The velocity deficits in the wake of the wind turbines are estimated using a simple wake model. The code is verified using the results from the previous studies. Results are obtained for three different wind regimes: (1) Constant wind speed and fixed wind direction, (2) constant wind speed and variable wind direction, and (3) variable wind speed and variable wind direction. Cost per unit power is reduced by 11.7% for Case 1, 11.8% for Case 2, and 15.9% for Case 3 for results obtained in this study. The advantages/benefits of a refined grid spacing of 1/40 rotor diameter (1 m) are evident and are discussed. To get an understanding of the sensitivity of the power produced to the wake model, optimized layout is obtained for the Case 1 using a different wake model.

Wind Farm Layout Optimization Using a Viral Systems Algorithm

2013 ◽  
Vol 4 (4) ◽  
pp. 27-40 ◽  
Author(s):  
Jose F. Espiritu ◽  
Carlos M. Ituarte-Villarreal
Keyword(s):  
Wind Speed ◽  
Wind Turbines ◽  
Wind Farm ◽  
Wind Farms ◽  
Optimal Number ◽  
Wind Park ◽  
Variable Direction ◽  
The Cost ◽  
Variable Wind ◽  
Large Wind

In the present research a new viral systems optimization algorithm is developed to find the optimal number and position of wind turbines in large wind farms with the main objective of minimizing the cost per unit power produced from the wind park. The developed algorithm is applied to three well known problems in literature which are: 1) constant wind speed and unidirectional uniform wind, 2) constant wind speed with variable direction, and 3) non-uniform variable wind speed with variable direction. In the final results, the distance between two wind turbines is reduced to one meter, compared to 5 rotor diameters in previous studies.

scholarly journals Performance Enhancement of Proposed Namaacha Wind Farm by Minimising Losses Due to the Wake Effect: A Mozambican Case Study

Energies ◽  
2021 ◽  
Vol 14 (14) ◽  
pp. 4291
Author(s):  
Paxis Marques João Roque ◽  
Shyama Pada Chowdhury ◽  
Zhongjie Huan
Keyword(s):  
Wind Speed ◽  
Wind Power ◽  
Wind Turbines ◽  
Power Output ◽  
Wind Farm ◽  
Wind Farms ◽  
Operating Conditions ◽  
Wake Effect ◽  
Wind Generators ◽  
Wind Potential

District of Namaacha in Maputo Province of Mozambique presents a high wind potential, with an average wind speed of around 7.5 m/s and huge open fields that are favourable to the installation of wind farms. However, in order to make better use of the wind potential, it is necessary to evaluate the operating conditions of the turbines and guide the independent power producers (IPPs) on how to efficiently use wind power. The investigation of the wind farm operating conditions is justified by the fact that the implementation of wind power systems is quite expensive, and therefore, it is imperative to find alternatives to reduce power losses and improve energy production. Taking into account the power needs in Mozambique, this project applied hybrid optimisation of multiple energy resources (HOMER) to size the capacity of the wind farm and the number of turbines that guarantee an adequate supply of power. Moreover, considering the topographic conditions of the site and the operational parameters of the turbines, the system advisor model (SAM) was applied to evaluate the performance of the Vestas V82-1.65 horizontal axis turbines and the system’s power output as a result of the wake effect. For any wind farm, it is evident that wind turbines’ wake effects significantly reduce the performance of wind farms. The paper seeks to design and examine the proper layout for practical placements of wind generators. Firstly, a survey on the Namaacha’s electricity demand was carried out in order to obtain the district’s daily load profile required to size the wind farm’s capacity. Secondly, with the previous knowledge that the operation of wind farms is affected by wake losses, different wake effect models applied by SAM were examined and the Eddy–Viscosity model was selected to perform the analysis. Three distinct layouts result from SAM optimisation, and the best one is recommended for wind turbines installation for maximising wind to energy generation. Although it is understood that the wake effect occurs on any wind farm, it is observed that wake losses can be minimised through the proper design of the wind generators’ placement layout. Therefore, any wind farm project should, from its layout, examine the optimal wind farm arrangement, which will depend on the wind speed, wind direction, turbine hub height, and other topographical characteristics of the area. In that context, considering the topographic and climate features of Mozambique, the study brings novelty in the way wind farms should be placed in the district and wake losses minimised. The study is based on a real assumption that the project can be implemented in the district, and thus, considering the wind farm’s capacity, the district’s energy needs could be met. The optimal transversal and longitudinal distances between turbines recommended are 8Do and 10Do, respectively, arranged according to layout 1, with wake losses of about 1.7%, land utilisation of about 6.46 Km2, and power output estimated at 71.844 GWh per year.

scholarly journals Results from a wake-steering experiment at a commercial wind plant: investigating the wind speed dependence of wake-steering performance

2021 ◽  
Vol 6 (6) ◽  
pp. 1427-1453
Author(s):  
Eric Simley ◽  
Paul Fleming ◽  
Nicolas Girard ◽  
Lucas Alloin ◽  
Emma Godefroy ◽  
...  
Keyword(s):  
Wind Speed ◽  
Wind Turbine ◽  
Wind Turbines ◽  
Wind Direction ◽  
Wind Farm ◽  
Poor Performance ◽  
Power Production ◽  
Wind Rose ◽  
Wind Speeds ◽  
Variability Model

Abstract. Wake steering is a wind farm control strategy in which upstream wind turbines are misaligned with the wind to redirect their wakes away from downstream turbines, thereby increasing the net wind plant power production and reducing fatigue loads generated by wake turbulence. In this paper, we present results from a wake-steering experiment at a commercial wind plant involving two wind turbines spaced 3.7 rotor diameters apart. During the 3-month experiment period, we estimate that wake steering reduced wake losses by 5.6 % for the wind direction sector investigated. After applying a long-term correction based on the site wind rose, the reduction in wake losses increases to 9.3 %. As a function of wind speed, we find large energy improvements near cut-in wind speed, where wake steering can prevent the downstream wind turbine from shutting down. Yet for wind speeds between 6–8 m/s, we observe little change in performance with wake steering. However, wake steering was found to improve energy production significantly for below-rated wind speeds from 8–12 m/s. By measuring the relationship between yaw misalignment and power production using a nacelle lidar, we attribute much of the improvement in wake-steering performance at higher wind speeds to a significant reduction in the power loss of the upstream turbine as wind speed increases. Additionally, we find higher wind direction variability at lower wind speeds, which contributes to poor performance in the 6–8 m/s wind speed bin because of slow yaw controller dynamics. Further, we compare the measured performance of wake steering to predictions using the FLORIS (FLOw Redirection and Induction in Steady State) wind farm control tool coupled with a wind direction variability model. Although the achieved yaw offsets at the upstream wind turbine fall short of the intended yaw offsets, we find that they are predicted well by the wind direction variability model. When incorporating the expected yaw offsets, estimates of the energy improvement from wake steering using FLORIS closely match the experimental results.

scholarly journals An Innovative Correction Method of Wind Speed for Efficiency Evaluation of Wind Turbines

ACTA IMEKO ◽  
2021 ◽  
Vol 10 (2) ◽  
pp. 46
Author(s):  
Alessio Carullo ◽  
Alessandro Ciocia ◽  
Gabriele Malgaroli ◽  
Filippo Spertino
Keyword(s):  
Wind Speed ◽  
Wind Turbines ◽  
Wind Farm ◽  
Southern Italy ◽  
Wind Farms ◽  
Correction Method ◽  
Turbine Rotor ◽  
Meteorological Station ◽  
Efficiency Evaluation ◽  
Horizontal Axis Wind Turbines

The performance of horizontal axis Wind Turbines (WTs) is strongly affected by the wind speed entering in their rotor. Generally, this quantity is not available, because the wind speed is measured on the nacelle behind the turbine rotor, providing a lower value. Therefore, two correction methods are usually employed, requiring two input quantities: the wind speed on the back of the turbine nacelle and the wind speed measured by a meteorological mast close to the turbines under analysis. However, the presence of this station in wind farms is rare and the number of WTs in the wind farm is high. This paper proposes an innovative correction, named “Statistical Method” (SM), that evaluates the efficiency of WTs by estimating the wind speed entering in the WTs rotor. This method relies on the manufacturer power curve and the data measured by the WT anemometer only, thus having the possibility to be also applied in wind farms without a meteorological station. The effectiveness of such a method is discussed by comparing the results obtained by the standard methods implemented on two turbines (rated power = 1.5 MW and 2.5 MW) of a wind power plant (nominal power = 80 MW) in Southern Italy.

scholarly journals Results from a Wake Steering Experiment at a Commercial Wind Plant: Investigating the Wind Speed Dependence of Wake Steering Performance

2021 ◽  
Author(s):  
Eric Simley ◽  
Paul Fleming ◽  
Nicolas Girard ◽  
Lucas Alloin ◽  
Emma Godefroy ◽  
...  
Keyword(s):  
Wind Speed ◽  
Wind Turbine ◽  
Wind Turbines ◽  
Wind Direction ◽  
Wind Farm ◽  
Poor Performance ◽  
Power Production ◽  
Wind Rose ◽  
Wind Speeds ◽  
Variability Model

Abstract. Wake steering is a wind farm control strategy in which upstream wind turbines are misaligned with the wind to redirect their wakes away from downstream turbines, thereby increasing the net wind plant power production and reducing fatigue loads generated by wake turbulence. In this paper, we present results from a wake steering experiment at a commercial wind plant involving two wind turbines spaced 3.7 rotor diameters apart. During the three-month experiment period, we estimate that wake steering reduced wake losses by 5.7 % for the wind direction sector investigated. After applying a long-term correction based on the site wind rose, the reduction in wake losses increases to 9.8 %. As a function of wind speed, we find large energy improvements near cut-in wind speed, where wake steering can prevent the downstream wind turbine from shutting down. Yet for wind speeds between 6–8 m/s, we observe little change in performance with wake steering. However, wake steering was found to improve energy production significantly for below-rated wind speeds from 8–12 m/s. By measuring the relationship between yaw misalignment and power production using a nacelle lidar, we attribute much of the improvement in wake steering performance at higher wind speeds to a significant reduction in the power loss of the upstream turbine as wind speed increases. Additionally, we find higher wind direction variability at lower wind speeds, which contributes to poor performance in the 6–8 m/s wind speed bin because of slow yaw controller dynamics. Further, we compare the measured performance of wake steering to predictions using the FLORIS (FLOw Redirection and Induction in Steady State) wind farm control tool coupled with a wind direction variability model. Although the achieved yaw offsets at the upstream wind turbine fall short of the intended yaw offsets, we find that they are predicted well by the wind direction variability model. When incorporating the predicted achieved yaw offsets, estimates of the energy improvement from wake steering using FLORIS closely match the experimental results.

Assessment of the noise produced by wind farms with the acoustically analogous techniques without stopping the noise source

2018 ◽  
Vol 43 (2) ◽  
pp. 201-209
Author(s):  
Gino Iannace ◽  
Amelia Trematerra ◽  
Umberto Berardi
Keyword(s):  
Wind Speed ◽  
Wind Turbine ◽  
Wind Turbines ◽  
Wind Farm ◽  
Noise Source ◽  
Sound Field ◽  
Wind Farms ◽  
Sound Level ◽  
Acoustic Measurements ◽  
Relative Contribution

In Italy, wind turbines with a nominal power below 1 MW can be installed following simplified authorization procedures and are therefore becoming the preferred choice for promoters. The assessment of the noise of wind farms is not easy, due to economic reasons, with it being difficult to stop and assess the relative contribution of each wind turbine. Several acoustic measurements were taken inside homes located near a wind farm consisting of three wind turbines, each with a nominal power of 1 MW. The acoustic measurements were taken by placing sound level meters inside the houses at different wind speed values and wind directions. The acoustic measurements were taken using the acoustically analogous place technique. For economic reasons, the plant cannot be switched off. In this case, the sound field generated by the operation of the wind turbines was measured by placing two sound level meters in a house.

scholarly journals Design and Analysis of a Wake Steering Controller with Wind Direction Variability

2019 ◽  
Author(s):  
Eric Simley ◽  
Paul Fleming ◽  
Jennifer King
Keyword(s):  
Turbulence Intensity ◽  
Wind Direction ◽  
Energy Production ◽  
Wind Farm ◽  
Wind Farms ◽  
Energy Gain ◽  
Steering Control ◽  
Modeling Tools ◽  
Wake Model ◽  
Variable Wind

Abstract. Wind farm control strategies are being developed to mitigate wake losses in wind farms, increasing energy production. Wake steering is a type of wind farm control in which a wind turbine's yaw position is misaligned from the wind direction, causing its wake to deflect away from downstream turbines. Current modeling tools used to optimize and estimate energy gains from wake steering are designed to represent wakes for fixed wind directions. However, wake steering controllers must operate in dynamic wind conditions and a turbine's yaw position cannot perfectly track changing wind directions. Research has been conducted on robust wake steering control optimized for variable wind directions. In this paper, the design and analysis of a wake steering controller with wind direction variability is presented for a two-turbine array using the FLOw Redirection and Induction in Steady State (FLORIS) control-oriented wake model. First, the authors propose a method for modeling the turbulent and low-frequency components of the wind direction, where the slowly varying wind direction serves as the relevant input to the wake model. Next, we explain a procedure for finding optimal yaw offsets for dynamic wind conditions considering both wind direction and yaw position uncertainty. We then performed simulations with the optimal yaw offsets applied using a realistic yaw offset controller in conjunction with a baseline yaw controller, showing good agreement with the predicted energy gain using the probabilistic model. Using the Gaussian wake model in FLORIS as an example, we compared the performance of yaw offset controllers optimized for static and dynamic wind conditions for different turbine spacings and turbulence intensity values. For a spacing of 5 rotor diameters and a turbulence intensity of 10 %, robust yaw offsets optimized for variable wind directions yielded an energy gain improvement of 128 %. In general, accounting for wind direction variability in the yaw offset optimization process was found to improve energy production more as the separation distance increased, whereas the relative improvement remained roughly the same for the range of turbulence intensity values considered.

scholarly journals Modified Power Curves for Prediction of Power Output of Wind Farms

Energies ◽  
2019 ◽  
Vol 12 (9) ◽  
pp. 1805 ◽  
Author(s):  
Mohsen Vahidzadeh ◽  
Corey D. Markfort
Keyword(s):  
High Resolution ◽  
Wind Speed ◽  
Wind Turbines ◽  
Power Output ◽  
Wind Farm ◽  
Sonic Anemometer ◽  
Wind Farms ◽  
Operating Conditions ◽  
Horizontal Axis Wind Turbine ◽  
Power Curves

Power curves are used to model power generation of wind turbines, which in turn is used for wind energy assessment and forecasting total wind farm power output of operating wind farms. Power curves are based on ideal uniform inflow conditions, however, as wind turbines are installed in regions of heterogeneous and complex terrain, the effect of non-ideal operating conditions resulting in variability of the inflow must be considered. We propose an approach to include turbulence, yaw error, air density, wind veer and shear in the prediction of turbine power by using high resolution wind measurements. In this study, two modified power curves using standard ten-minute wind speed and high resolution one-second data along with a derived power surface were tested and compared to the standard operating curve for a 2.5 MW horizontal axis wind turbine. Data from supervisory control and data acquisition (SCADA) system along with wind speed measurements from a nacelle-mounted sonic anemometer and wind speed measurements from a nearby meteorological tower are used in the models. The results show that all of the proposed models perform better than the standard power curve while the power surface results in the most accurate power prediction.

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