Performance analysis of wind resource assessment programs in complex terrain

Llombart, A.; Talayero, A.; Mallet, Alfred; Telmo, E.; Circe, Fundacion; Luna, Maria de

doi:10.24084/repqj04.430

Cited by 8 publications

(7 citation statements)

References 4 publications

(1 reference statement)

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“…Palllabazzer [5] provided simple method for evaluating energy output of wind turbine. A. Lombart [10] and his team worked for optimal siting of wind turbines and discussed wind resource evaluation methods. R. K. Pachuari [7] emphasis on assessment of wind energy potential.…”

Section: Literature Reviewmentioning

confidence: 99%

Performance Evaluation of Wind Turbines on the basis of Power Generation

Tiwari¹,

Gupta²

2019

IJRTE

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Wind energy is one of the promising solution for future demand and need of energy. Main application of wind energy is in electricity sector. India is having potential for wind installation which is not yet fully explored. This paper compares the results from test rig small scale wind turbine and scale up data for different sizes of turbine as power from small size turbine was very low. A mathematical formulation for wind power is developed for finding total power from the available wind at certain speed. Three factors considered for analysis of three responses. Factors are Wind Speed, Rotor diameter and Swept area for getting response for wind power, torque and wind power density. For different wind speed and different rotor diameter wind power, torque and wind power density calculations done. Response surface method is used to design the experiments and ANOVA (Analysis of Variance) is used to analyze that experiment results are significant or not. Wind data analysis shows that wind speed gives a fairly good response in the range of wind speed 8m/s to 12m/s in practical grounds.

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Section: Literature Reviewmentioning

confidence: 99%

Performance Evaluation of Wind Turbines on the basis of Power Generation

Tiwari¹,

Gupta²

2019

IJRTE

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“…WindSim, a CFD package for wind resource assessment and park optimization, has been used and evaluated in both industrial settings and academia. Several groups compared the results of WindSim to WAsP over complex terrain and found better performance in the CFD model WindSim [2,5,6]. Other studies validated the WindSim results against measurements without comparing to linear models [3,[7][8][9].…”

Section: Introductionmentioning

confidence: 99%

Wind Energy Prediction in Highly Complex Terrain by Computational Fluid Dynamics

2019

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With rising levels of wind power penetration in global electricity production, the relevance of wind power prediction is growing. More accurate forecasts reduce the required total amount of energy reserve capacity needed to ensure grid reliability and the risk of penalty for wind farm operators. This study analyzes the Computational Fluid Dynamics (CFD) software WindSim regarding its ability to perform accurate wind power predictions in complex terrain. Simulations of the wind field and wind farm power output in the Swiss Jura Mountains at the location of the Juvent Wind Farm during winter were performed. The study site features the combined presence of three complexities: topography, heterogeneous vegetation including forest, and interactions between wind turbine wakes. Hence, it allows a comprehensive evaluation of the software. Various turbulence models, forest models, and wake models, as well as the effects of domain size and grid resolution were evaluated against wind and power observations from nine Vestas V90's 2.0-MW turbines. The results show that, with a proper combination of modeling options, WindSim is able to predict the performance of the wind farm with sufficient accuracy.

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“…Researchers built more than 120 terrain models from data of heights and roughness covering the Norwegian coast from southern Lindesnes to the northern boundary with Russia and subsequently used WindSim to evaluate wind resources on the Norwegian coast [26]. According to a number of scientific research studies and practical engineering experiments, CFD-based calculation software can simulate wind resources more accurately than WAsP [27][28][29].…”

Section: Introductionmentioning

confidence: 99%

Prediction of Power Generation by Offshore Wind Farms Using Multiple Data Sources

Yue

Liu

et al. 2019

Energies

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In this study we evaluated the wind resources of wind farms in the Changhua offshore area of Taiwan. The offshore wind farm in Zone of Potential (ZoP) 26 was optimized through an economic evaluation. The annual energy production (AEP) of the offshore wind farm in ZoP 26 was predicted for 10 and 25 years with probabilities of 50%, 75%, and 90% by using measured mast data, measure-correlate-predict (MCP) data derived from Modern-Era Retrospective Analysis for Research and Applications (MERRA), and Central Weather Bureau (CWB) data. When the distance between the turbines in a wind farm was decreased from 12D to 6D, the turbine number increased from 53 to 132, while the capacity factor decreased slightly from 48.6% to 47.6%. MCP data derived from the inland CWB station with similar levels of wind resources can be used to accurately predict the power generation of the target offshore wind farm. The use of MCP with mast data as target data, together with CWB and MERRA data as reference data, proved to be a feasible method for predicting offshore wind power generation in places where a mast is available in a neighboring area.

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Performance analysis of wind resource assessment programs in complex terrain

Cited by 8 publications

References 4 publications

Performance Evaluation of Wind Turbines on the basis of Power Generation

Performance Evaluation of Wind Turbines on the basis of Power Generation

Wind Energy Prediction in Highly Complex Terrain by Computational Fluid Dynamics

Prediction of Power Generation by Offshore Wind Farms Using Multiple Data Sources

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