This document discusses wind resource assessment for wind farm development. It covers how wind is generated, accessing wind resources through measurement and modeling, and estimating energy production with uncertainties. Key steps include measuring wind speeds on site, correlating to long-term reference stations to predict long-term distributions, modeling wind flows, planning turbine layouts, and estimating annual energy yields while accounting for production losses and uncertainties. Accurate wind assessment is critical for maximizing energy production estimates and ensuring project viability.
Wind Power History
Advantages & Disadvantages
Wind Turbine & Components
Power From Wind Mill
Swept area Of Wind Mill Rotor
Wind Speed Variation with Height
Density & Temperature Variation with Height
Global Wind Patterns
Wind Speed Measurements
Wind Speed Distribution
Weibull Probability Distributions
In this presentation a brief introduction is given on parts of wind turbine, classification of wind turbines, importance of wind turbines, current status like installed capacity (annual and cumulative) . Then there is a explanation on theory behind the design of wind turbine blades i.e, AERODYNAMICS OF WIND TURBINES which includes explanation about shape of an aerofoil, its different parameters, lift force, drag force, different equations about lift drag force, NACA profiles, Blade Element Momentum Theory, etc.
Wind Power History
Advantages & Disadvantages
Wind Turbine & Components
Power From Wind Mill
Swept area Of Wind Mill Rotor
Wind Speed Variation with Height
Density & Temperature Variation with Height
Global Wind Patterns
Wind Speed Measurements
Wind Speed Distribution
Weibull Probability Distributions
In this presentation a brief introduction is given on parts of wind turbine, classification of wind turbines, importance of wind turbines, current status like installed capacity (annual and cumulative) . Then there is a explanation on theory behind the design of wind turbine blades i.e, AERODYNAMICS OF WIND TURBINES which includes explanation about shape of an aerofoil, its different parameters, lift force, drag force, different equations about lift drag force, NACA profiles, Blade Element Momentum Theory, etc.
Enjoy different views of water and the special message and wonderful singing by Ben Vaughn and Alex Cook who have allowed me to use their copyrighted music.
Validation of wind resource assessment process based on CFD Jean-Claude Meteodyn
Wind resource assessment requires nowadays more efficient tools to provide an accurate evaluation of production in order to reduce costs.As onshore wind farms are built in more complex terrains, it is necessary to find a new method to provide a fine evaluation of energy which reduces the error during the data extrapolation process. This explains why CFD models have become a standard for WRA in specific conditions.This presentation is focused on the wind speed and energy yield prediction carried out for a 29MW wind farm project. The accuracy of the wind modeling is investigated by the cross validation between the different met masts around the site. The net energy prediction P50 is compared against real wind farm performance data during a blind test organized by EWEA in 2013. More than 50 companies have been involved in order to compare methods results.
Wind energy forecasting using radial basis function neural networkseSAT Journals
Abstract
Wind power forecast is essential for a wind farm developer for comprehensive assessment of wind potential at a particular site or
topographical location. Wind energy potential at any given location is a non –linear function of mean average wind speed,
vertical wind profile, energy pattern factor, peak wind speed, prevailing wind direction, lull hours, air density and a few other
parameters. Wind energy pattern data of various locationsis collected from a published resource data book by Centre for Wind
Energy Technology, India.Modeling of wind energy forecasting problem consists of data collection, input-output selection,
mappingand simulation. In this work, artificial neural networks technique is adopted to deal with the wind energy forecasting
problem.After normalization, neural network will be run with training dataset.Radial Basis function based Neural Networks is a
feed-forward algorithm of artificial neural networks that offers supervised learning.It establishes local mapping with two fold
learning quickly.Wind power densities predicted for new locationsare in agreement with the measured values atthewind
monitoring stations.MAPE was found out to be less than 10% for all the values of Wind Power Density predictions at new
topographical locations and R2 is found to be nearer to unity.WPD values are multiplied by wind availability hours (generation
hours) in that particular location to give number of energy units at the turbine output. These values are compared to the output of
the wind turbine model installed in the same region, so as to assess the number of units generated by that particular wind turbine
in the respective locations.This kind of assessment is useful for wind energy projects during feasibility studies. With this work, it is
established that radial basis function neural netscan be used as a diagnostic tool for function approximation problemsconnected
towind energy resourcemodeling& forecast.
Keywords: Wind power density, wind energy, forecast, modeling, air density, peak wind speed, radial basis function,
neural network, CoD, MAPE
In developing complex engineering systems, model-based design approaches often face critical challenges due to pervasive uncertainties and high computational expense. These challenges could be alleviated to a significant extent though informed modeling decisions, such as model substitution, parameter estimation, localized re-sampling, or grid refine- ment. Informed modeling decisions therefore necessitate (currently lacking) design frame- works that effectively integrate design automation and human decision-making. In this paper, we seek to address this necessity in the context of designing wind farm layouts, by taking an information flow perspective of this typical model-based design process. Specif- ically, we develop a visual representation of the uncertainties inherited and generated by models and the inter-model sensitivities. This framework is called the Visually-Informed Decision-Making Platform (VIDMAP) for wind farm design. The eFAST method is used for sensitivity analysis, in order to determine both the first-order and the total-order in- dices. The uncertainties in the independent inputs are quantified based on their observed variance. The uncertainties generated by the upstream models are quantified through a Monte Carlo simulation followed by probabilistic modeling of (i) the error in the output of the models (if high-fidelity estimates are available), or (ii) the deviation in the outputs estimated by different alternatives/versions of the model. The GUI in VIDMAP is cre- ated using value-proportional colors for each model block and inter-model connector, to respectively represent the uncertainty in the model output and the impact (downstream) of the information being relayed by the connector. Wind farm layout optimization (WFLO) serves as an excellent platform to develop and explore VIDMAP, where WFLO is generally performed using low fidelity models, as high-fidelity models (e.g. LES) tend to be compu- tationally prohibitive in this context. The final VIDMAP obtained sheds new light into the sensitivity of wind farm energy estimation on the different models and their associated uncertainties.
Impact of Different Wake Models on the Estimation of Wind Farm Power GenerationWeiyang Tong
For citations, please refer to the journal version of this paper,
by Tong et al., "Sensitivity of Wind Farm Output to Wind Conditions, Land Configuration, and Installed Capacity, Under Different Wake Models", J. Mech. Des. 137(6), 061403 (Jun 01, 2015) (11 pages), Paper No: MD-14-1339; doi: 10.1115/1.4029892
available at:
http://mechanicaldesign.asmedigitalcollection.asme.org/article.aspx?articleid=2173776
Among the Renewable Energy Sources, Wind Energy is taken up with careful prior efforts before implementation as it requires all capital and technical inputs before payback starts. However, it is a clean source of electric power compared to coal based thermal power. India is a country that has made progress in wind power investment.
Drivers and Barriers in the current CSP marketLeonardo ENERGY
This webinar will provide a general view of drivers and barriers for CSP development, with a particular focus on the structure of the CSP Value Chain. From a technical point of view, the main key performances will be reviewed for the different technologies.
Wind Power Density Analysis for Micro-Scale Wind Turbinestheijes
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The Supply Chain – The Demand Side Wolfe Island Shoals A Case Study in Offshore Wind Farm Requirements There Are No Insurmountable Problems That Are Not Golden Opportunities Presented by: Uwe Roeper / Andrew Chant
Understanding Challenges Facing Small Wind Projectsmtingle
Understanding Challenges Facing
Small Wind Projects
OPA Technology Symposium
Waterloo, Ontario
June 18, 2010
By: Andrew Chant
With the Assistance of: Alex Tsopelas
Bonnie Choi
13. R. M. Young Wind Monitor Features Propeller-type anemometer with fuselage and tail wind vane Rugged design for use in a variety of climates worldwide Manufactured by R. M. Young Specifications Wind Speed Range: 0-134 mph (0-60 m/s) Accuracy: ±0.6 mph (0.3 m/s) Starting threshold: 2.2 mph (1.0 m/s) Gust survival: 220 mph (100 m/s) Wind Direction Range: 0-360° mechanical, 355° electrical (5° open) Accuracy: ±3° Starting threshold at 10° displacement: 2.2 mph (1.1 m/s)
14. CR800 Measurement and Control Datalogger Features Ideal datalogger where only a few sensors will be measured Stores 4 Mbytes of data and programming in SRAM Data format is table Operating system: PakBus® Software support offered in LoggerNet or PC400 (full-featured) or ShortCut (programming) Detachable keyboard/display, the CR1000KD, can be carried to multiple stations Supports Modbus protocol, SDI-12 protocol, and SDM devices Specifications Analog inputs: 6 single-ended or 3 differential, individually configured Pulse counters: 2 Switched voltage excitations: 2 Control/digital ports: 4 Scan rate: 100 Hz Analog voltage resolution: to 0.33 µV A/D bits: 13
15.
16. Power Law Profile - use wind speed measurements at two heights to find α - then use a to calculate wind speed at hub height α is the power law exponent (wind shear exponent) u R is the wind speed at height z r
power law should be carefully employed since it is not a physical representation of the surface layer and does not describe the flow nearest to the ground very well (i.e. should only be used for heights above the roughness elements where the flow is free)
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