scholarly journals Advanced Wind Resource Characterization and Stationarity analysis for Improved Wind Farm Siting

10.5772/17213 ◽  
2011 ◽  
Author(s):  
Mark Morrissey ◽  
Scott Greene
Keyword(s):  
Wind Farm ◽  
Wind Resource

scholarly journals Improving accuracy of wind resource assessment through feedback loops of operational performance data: A South African case study

2019 ◽  
Vol 30 (3) ◽  
pp. 1-10
Author(s):  
D. Pullinger ◽  
A. Ali ◽  
M. Zhang ◽  
N. Hill ◽  
T. Crutchley
Keyword(s):  
South African ◽  
Wind Farm ◽  
Wind Farms ◽  
Resource Assessment ◽  
Performance Data ◽  
Operational Performance ◽  
Energy Yield ◽  
Cause Of Error ◽  
Improving Accuracy ◽  
Wind Resource

This study addresses two key objectives using operational performance data from most of the Round 1 wind farms connected to the grid in South Africa: benchmarking of wind farm performance and validation of the pre-construction energy yield assessments. These wind farms were found to perform in line with internationally reported levels of wind farm availability, with a mean energy-based availability of 97.8% during the first two years of operation. The pre-construction yield assessments used for financing in 2012 were found to over-predict project yield (P50) by 4.9%. This was consistent with other validation studies for Europe and North America. It was also noted that all projects exceed the pre-construction P90 estimate. The reasons for this discrepancy were identified, with the largest cause of error being wind flow and wake-modelling errors. Following a reassessment using up to date methodologies from 2018, the mean bias in pre-construction predictions was 1.4%.

scholarly journals An Optimization Framework for Wind Farm Design in Complex Terrain

2018 ◽  
Vol 8 (11) ◽  
pp. 2053 ◽  
Author(s):  
Ju Feng ◽  
Wen Shen ◽  
Ye Li
Keyword(s):  
Complex Terrain ◽  
Wind Farm ◽  
Search Algorithm ◽  
Random Search ◽  
Wind Farms ◽  
Minimal Distance ◽  
Original Design ◽  
Optimization Framework ◽  
Random Search Algorithm ◽  
Wind Resource

Designing wind farms in complex terrain is an important task, especially for countries with a large portion of complex terrain territory. To tackle this task, an optimization framework is developed in this study, which combines the solution from a wind resource assessment tool, an engineering wake model adapted for complex terrain, and an advanced wind farm layout optimization algorithm. Various realistic constraints are modelled and considered, such as the inclusive and exclusive boundaries, minimal distances between turbines, and specific requirements on wind resource and terrain conditions. The default objective function in this framework is the total net annual energy production (AEP) of the wind farm, and the Random Search algorithm is employed to solve the optimization problem. A new algorithm called Heuristic Fill is also developed in this study to find good initial layouts for optimizing wind farms in complex terrain. The ability of the framework is demonstrated in a case study based on a real wind farm with 25 turbines in complex terrain. Results show that the framework can find a better design, with 2.70% higher net AEP than the original design, while keeping the occupied area and minimal distance between turbines at the same level. Comparison with two popular algorithms (Particle Swarm Optimization and Genetic Algorithm) also shows the superiority of the Random Search algorithm.

scholarly journals Could Crop Height Affect the Wind Resource at Agriculturally Productive Wind Farm Sites?

2015 ◽  
Vol 158 (3) ◽  
pp. 409-428 ◽  
Author(s):  
Brian Vanderwende ◽  
Julie K. Lundquist
Keyword(s):  
Wind Farm ◽  
Crop Height ◽  
Wind Resource

scholarly journals Statistical characterization of roughness uncertainty and impact on wind resource estimation

2016 ◽  
Author(s):  
Mark Kelly ◽  
Hans Ejsing Jørgensen
Keyword(s):  
Wind Farm ◽  
Statistical Characterization ◽  
Wind Speeds ◽  
Industry Standard ◽  
Industry Practice ◽  
Mean Wind Speed ◽  
Atlas Method ◽  
Wind Farm Location ◽  
Drag Law ◽  
Wind Resource

Abstract. In this work we relate uncertainty in background roughness length (z0) to uncertainty in wind speeds, where the latter are predicted at a wind farm location based on wind statistics observed at a different site. Sensitivity of predicted winds to roughness is derived analytically for the industry-standard European Wind Atlas method, which is based on the geostrophic drag law. We consider roughness statistically and its corresponding uncertainty, in terms of both z0 derived from measured wind speeds, as well as that chosen in practice by wind engineers. We show the combined effect of roughness uncertainty arising from differing wind-observation and turbine-prediction sites; this is done for the case of roughness bias, as well as for the general case. For estimation of uncertainty in annual energy production (AEP), we also develop a generalized analytical turbine power curve, from which we derive a relation between mean wind speed and AEP. Following from our developments we provide guidance on approximate roughness uncertainty magnitudes to be expected in industry practice, and also find that sites with larger background roughness incur relatively larger uncertainties.

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