scholarly journals Improving mid-altitude mesoscale wind speed forecasts using LiDAR-based observation nudging for Airborne Wind Energy Systems

2019 ◽  
Author(s):  
Markus Sommerfeld ◽  
Curran Crawford ◽  
Gerald Steinfeld ◽  
Martin Dörenkämper
Keyword(s):  
Wind Speed ◽  
Wind Energy ◽  
Atmospheric Stability ◽  
Energy Systems ◽  
Data Set ◽  
Diurnal Variability ◽  
Advantages And Disadvantages ◽  
Lidar Measurements ◽  
Wind Energy Systems ◽  
The Impact

Abstract. Airborne wind energy systems (AWES) aim to operate at altitudes above conventional wind turbines where reliable high resolution wind data is scarce. Wind LiDAR measurements and mesoscale models both have their advantages and disadvantages when assessing the wind resource at such heights. This article investigates whether assimilating measurements into the mesoscale WRF model using observation nudging generates a more accurate, complete data set. The impact of continuous observation nudging at multiple altitudes on simulated wind conditions is compared to an unnudged reference run and to the LiDAR measurements themselves. We compare the impact on wind speed and direction for individual days, average diurnal variability and long term statistics. Finally, wind speed data is used to estimate optimal traction power and operating altitudes of AWES. Observation nudging improves the overall accuracy of WRF. Close to the surface the impact of nudging is limited as effects of the air-surface interaction dominate, but becomes more prominent at mid-altitudes and decreases towards high altitudes. The wind speed probability distribution shows a multi-modality caused by changing atmospheric stability conditions. Based on a simplified AWES model the most probable optimal altitude will be around 400 m. Such systems will benefit from dynamically adjusting their operating altitude.

scholarly journals Improving mesoscale wind speed forecasts using lidar-based observation nudging for airborne wind energy systems

2019 ◽  
Vol 4 (4) ◽  
pp. 563-580
Author(s):  
Markus Sommerfeld ◽  
Martin Dörenkämper ◽  
Gerald Steinfeld ◽  
Curran Crawford
Keyword(s):  
Wind Speed ◽  
Wind Energy ◽  
Energy Systems ◽  
Stability Conditions ◽  
Data Set ◽  
Advantages And Disadvantages ◽  
Wide Range ◽  
Lidar Measurements ◽  
Wind Energy Systems ◽  
The Impact

Abstract. Airborne wind energy systems (AWESs) aim to operate at altitudes above conventional wind turbines where reliable high-resolution wind data are scarce. Wind light detection and ranging (lidar) measurements and mesoscale models both have their advantages and disadvantages when assessing the wind resource at such heights. This study investigates whether assimilating measurements into the mesoscale Weather Research and Forecasting (WRF) model using observation nudging generates a more accurate, complete data set. The impact of continuous observation nudging at multiple altitudes on simulated wind conditions is compared to an unnudged reference run and to the lidar measurements themselves. We compare the impact on wind speed and direction for individual days, average diurnal variability and long-term statistics. Finally, wind speed data are used to estimate the optimal traction power and operating altitudes of AWES. Observation nudging improves the WRF accuracy at the measurement location. Close to the surface the impact of nudging is limited as effects of the air–surface interaction dominate but becomes more prominent at mid-altitudes and decreases towards high altitudes. The wind speed frequency distribution shows a multi-modality caused by changing atmospheric stability conditions. Therefore, wind speed profiles are categorized into various stability conditions. Based on a simplified AWES model, the most probable optimal altitude is between 200 and 600 m. This wide range of heights emphasizes the benefit of such systems to dynamically adjust their operating altitude.

Application of the Maximum Entropy Method for Determining a Sensitive Distribution in the Renewable Energy Systems

2015 ◽  
Vol 137 (4) ◽  
Author(s):  
Gholamhossein Yari ◽  
Zahra Amini Farsani
Keyword(s):  
Renewable Energy ◽  
Wind Speed ◽  
Wind Energy ◽  
Maximum Entropy ◽  
Energy Systems ◽  
Rayleigh Distribution ◽  
Entropy Method ◽  
Climatic Data ◽  
Data Set ◽  
Renewable Energy Systems

In the field of the wind energy conversion, a precise determination of the probability distribution of wind speed guarantees an efficient use of the wind energy and enhances the position of wind energy against other forms of energy. The present study thus proposes utilizing an accurate numerical-probabilistic algorithm which is the combination of the Newton’s technique and the maximum entropy (ME) method to determine an important distribution in the renewable energy systems, namely the hyper Rayleigh distribution (HRD) which belongs to the family of Weibull distribution. The HRD is mainly used to model the wind speed and the variations of the solar irradiance level with a negligible error. The purpose of this research is to find the unique solution to an optimization problem which occurs when maximizing Shannon’s entropy. To confirm the accuracy and efficiency of our algorithm, we used the long-term data for the average daily wind speed in Toyokawa for 12 yr to examine the Rayleigh distribution (RD). This data set was obtained from the National Climatic Data Center (NCDC) in Japan. It seems that the RD is more closely fitted to the data. In addition, we presented different simulation studies to check the reliability of the proposed algorithm.

scholarly journals Normal law of distribution of wind velocity vector in polar coordinates

2017 ◽  
pp. 5-17
Author(s):  
E. P. Shkolnyy
Keyword(s):  
Wind Speed ◽  
Wind Energy ◽  
Polar Angle ◽  
Energy Systems ◽  
Specific Area ◽  
Polar Coordinates ◽  
Conditional Distributions ◽  
Speed Regime ◽  
Normal Law ◽  
Wind Energy Systems

It is impossible to organize wind energy systems without studying of wind speed regime at the surface layer of the atmosphere within a specific area and at climatic scales. Such studies are often accompanied by approximations of probabilities of wind speed performed in the form of normal law of a system of random values presented by a zonal u and a meridional u which are components of a wind speed vector. It is suggested that, for the purposes of wind energy, display of a wind speed vector in polar coordinates (r, α) where r is a module of wind speed and α is a polar angle appears to be more preferable. The article shows a transform from a normal law of distribution of probabilities with density f(u,u) to a normal law distribution with density f(r,α) completed by means of functional transformation with elliptic dispersion in place. Based on a normal law of distribution f(r,α) and through integration with respect to corresponding variables individual distributions of probabilities f(r) and f(α)  as well as conditional distributions of probabilities f(r/α)  and f(α/r)  were obtained in the areas of their existence. The article shows individual distributions in case of circular and elliptic dispersion of a wind speed vector. It shows that an individual distribution of a wind speed probability in case of circular dispersion and in the absence of correlated dependence turns into the Rayleigh's distribution and a conditional distribution of a polar angle degenerates in an even distribution. The cases of distributions with dispersions of a wind speed module having elliptic properties subject to availability of correlated connection between wind speed components were also studied. Calculation of probabilities of a polar angle being within different sections of the area 0≤α≤2π with set values of a wind speed module also took place. Numerical experiments proved the advantage of such modeling of distributions of wind speed vector.

Altitude and Crosswind Motion Control for Optimal Power-Point Tracking in Tethered Wind Energy Systems With Airborne Power Generation

2013 ◽  
Author(s):  
Chris Vermillion
Keyword(s):  
Wind Speed ◽  
Wind Energy ◽  
Motion Control ◽  
Energy Systems ◽  
Energy System ◽  
Control Laws ◽  
Point Tracking ◽  
Optimal Power ◽  
Power Point ◽  
Wind Energy Systems

This paper presents a control strategy that combines altitude and crosswind motion control for tethered wind energy systems with airborne turbines and generators. The proposed algorithm adjusts altitude and induces an appropriate level of crosswind motion to present the system with an apparent wind speed that most closely meets, but does not exceed, the rated wind speed of the on-board turbine(s), thereby tracking the turbine’s optimal power point. The adjustment of both altitude and motion control, along with the reduction in crosswind motion and altitude when the rated wind speed is exceeded, differentiates the proposed control architecture from other strategies proposed in the literature. Initial control laws and simulation results are presented for the Altaeros lighter-than-air wind energy system.

ON POTENTIAL OF USING WIND TURBINES WITH COAXIAL WIND ROTORS FOR AUTONOMOUS POWER SUPPLY

2018 ◽  
pp. 25-33
Author(s):  
L. I. Knysh
Keyword(s):  
Wind Speed ◽  
Wind Energy ◽  
Wind Turbine ◽  
Wind Turbines ◽  
Power Supply ◽  
Energy Systems ◽  
Experimental Setup ◽  
Return Flows ◽  
Turbine Capacity ◽  
Wind Energy Systems

The paper presents the experimental research results for the horizontal-axis wind turbine with coaxial wind rotors. It is assumed that such coaxial layout of the wind turbine can be used for designing of the wind energy systems with relatively low capacity and limited location area since the coaxial systems have advantages in overall dimensions and maximum using of the swept area. Possibility of coaxial horizontal-axis wind turbines usage is determined by positive or negative effect of turbines on each other. Literature review shows that closely spaced wind turbines can generally improve flow characteristics under certain conditions and consequently increase wind energy system efficiency. We have carried out the experiments in T-5 wind tunnel with two coaxial model two-bladed wind turbines which rotate in opposite directions. The generator of the first turbine and first turbine itself are located on the same shaft in the test section of wind tunnel. The second generator is in a lower compartment of the experimental setup and is connected by the transmission. We have measured the dynamic, energy and frequency characteristics of wind energy systems based on created experimental setup. A Pitot tube and automatic metering devises have measured the dynamic parameters and energy performance respectively. A frequency counter has saved all of the data obtained with the laser frequency measurement technique. The experiment has some specific technical features so the data received need to be corrected. The coaxial wind turbine power has decreased in comparison to isolated wind turbine at low wind speed. The return flows reinforce turbulence so wind speed falls. If wind speed increases, the impact of the return flows decreases, the coaxial wind turbine capacity significantly grows and exceeds isolated turbine capacity. The possibility of using wind turbines with coaxial wind rotors for autonomous power supply is shown. Such wind turbines are perspective and require more detailed analysis.

scholarly journals Time Series Forecasting for Wind Energy Systems Based on High Order Neural Networks

Mathematics ◽  
2021 ◽  
Vol 9 (10) ◽  
pp. 1075
Author(s):  
Alma Y. Alanis ◽  
Oscar D. Sanchez ◽  
Jesus G. Alvarez
Keyword(s):  
Neural Network ◽  
Time Series ◽  
Wind Speed ◽  
Wind Energy ◽  
Energy Systems ◽  
High Order ◽  
Energy Price ◽  
On Line ◽  
Wind Energy Systems ◽  
Power And Energy

Wind energy is one of the most promising alternatives as energy sources; however, to obtain the best results, producers need to forecast the wind speed, generated power and energy price in order to provide the appropriate tools for optimal operation, planning, control and marketing both for isolated wind systems and for those that are interconnected to a main distribution network. For the present work, a novel methodology is proposed for the forecasting of time series in wind energy systems; it consists of a high-order neural network that is trained on-line by the extended Kalman filter algorithm. Unlike most modern artificial intelligence methods of forecasting, which are based on hybridizations, data pre-filtering or deep learning methods, the proposed method is based on the simplicity of implementation, low computational complexity and real-time operation to produce 15-step-ahead forecasting in a time series of wind speed, generated power and energy price. The proposed scheme has been evaluated using real data from open access repositories of wind farms. The results show that an on-line training of the neural network produces high precision, without the need for any other information beyond a few past observations.

Sign in / Sign up

Export Citation Format

Share Document