Contributions to wind farm power estimation considering wind direction-dependent wake effects

Wind Energy ◽  
2016 ◽  
Vol 20 (2) ◽  
pp. 221-231 ◽  
Author(s):  
Andrés Feijóo ◽  
Daniel Villanueva
2014 ◽  
Vol 573 ◽  
pp. 777-782 ◽  
Author(s):  
A.Jeya Saravanan ◽  
C.P. Karthikeyan ◽  
Anand A. Samuel

Uneven terrains in mountain regions, where wind mills are to be erected cause concerns on the matrix of location, variation in wind direction, wake effects and due to location which may take a toll on efficiency, frequent changes in wind velocity, limitation of the hub height are a fear of the exogenous variables that influence the operation of wind farm. An attempt is made in this work to analyze the effect of those parameters on the efficiency of wind farm. Energy efficiency and exergy efficiency for a three column wind farm are determined and compared. The mathematical model developed considers wake deficit loss, transmission losses and resource losses the loss due to change in the wind direction, overall efficiency factor and locational specifications. A new objective function is derived for the wind farm with multidirectional wind flow and it is solved by Covariant Matrix Adaptation Evolutionary Strategy algorithm. This algorithm is used to maximize the wind farm exergetic efficiency. Location specification is the main variable to optimize and the other dimensionless variables remain same. Exergy efficiency is improved when compared to the reference layouts. The results projected will help the wind farm promoters to optimally utilize the resources to get maximum output.


Author(s):  
Xiaomin Chen ◽  
Ramesh Agarwal

In this paper, we consider the Wind Farm layout optimization problem using a genetic algorithm. Both the Horizontal–Axis Wind Turbines (HAWT) and Vertical-Axis Wind Turbines (VAWT) are considered. The goal of the optimization problem is to optimally place the turbines within the wind farm such that the wake effects are minimized and the power production is maximized. The reasonably accurate modeling of the turbine wake is critical in determination of the optimal layout of the turbines and the power generated. For HAWT, two wake models are considered; both are found to give similar answers. For VAWT, a very simple wake model is employed.


2016 ◽  
Vol 38 ◽  
pp. 477
Author(s):  
Thays Paes de Oliveira ◽  
Rosiberto Salustiano da Silva Junior ◽  
Roberto Fernando Fonseca Lyra ◽  
Sandro Correia Holanda

Wind energy is seen as one of the promising generation of electricity, as a source of cheap and renewable, is benefit to reduce the environmental impacts of the dam. Along with the hydroelectric networks, the energy produced by the wind will help to increase power generation capacity in the country. That from speed data and direction municipality Wind Craíbas in the corresponding period 2014 - 2015, estimated the wind potential of the region. The tool used in the treatment of the collected data was the Wasp, making simulations of three different levels of measurement, producing a fictitious wind farm with powerful wind turbine. With the model, WASP helps estimate the probability distribution of Weibull and scale parameters A and K. he predominant wind direction is southeast and the best wind power and intensity density levels took place in 70m and 100m high , with about 201 W / m² and 243 W / m² respectively. But when evalua ted the inclusion of fictitious wind farm, the best use happened at 100m tall with production around 73.039 GWh , which can be attributed this improvement to increased efficiency of the wind turbine used in the simulation.


2014 ◽  
Vol 933 ◽  
pp. 384-389
Author(s):  
Xin Zhao ◽  
Shuang Xin Wang

Wind power short-term forcasting of BP neural network based on the small-world optimization is proposed. First, the initial data collected from wind farm are revised, and the unreasonable data are found out and revised. Second, the small-world optimization BP neural network model is proposed, and the model is used on the prediction method of wind speed and wind direction, and the prediction method of power. Finally, by simulation analysis, the NMAE and NRMSE of the power method are smaller than those of the wind speed and wind direction method when the wind power data of one hour later are predicted. When the power method are used to forecast the data one hour later, NMAE is 5.39% and NRMSE is 6.98%.


Author(s):  
Genevieve M. Starke ◽  
Paul Stanfel ◽  
Charles Meneveau ◽  
Dennice F. Gayme ◽  
Jennifer King

2019 ◽  
Vol 122 ◽  
pp. 04005
Author(s):  
Ilayda Ulku ◽  
Cigdem Alabas-Uslu

A wind farm, mainly, is composed of a set of turbines, one or more transmitters and a set of electrical cable connections between turbines and transmitters. Determination of turbine locations within the farm to maximize total power generation is called turbine location (TL) problem. Relative turbine positions affect the amount of overall energy because of wake effects. Determination of cable connections among turbines and transmitters to collect produced energy by turbines at transmitters is called cable layout (CL) problem. While TL problem is directly effective on the total energy production in the farm, CL problem indirectly affects the total energy due to the power losses. In the literature, TL and CL problems are solved sequentially where the layout found by solving of TL is used as an input of CL problem. To minimize wake effects in TL problem, distances between turbine pairs should be increased, however, as the distances are increased the cable cost increases in CL problem. A new mathematical model is developed to deal with simultaneously solving of TL and CL problems. A set of test instances are used to show the performance of the proposed model. The experiments show the practical use of the proposed holistic model.


2015 ◽  
Vol 9 (3) ◽  
pp. 954-965 ◽  
Author(s):  
Abdul Motin Howlader ◽  
Tomonobu Senjyu ◽  
Ahmed Yousuf Saber

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