A Comprehensive Measure of the Energy Resource Potential of a Wind Farm Site

2011 ◽  
Author(s):  
Jie Zhang ◽  
Souma Chowdhury ◽  
Achille Messac ◽  
Luciano Castillo
Keyword(s):  
Power Generation ◽  
Wind Speed ◽  
Velocity Distribution ◽  
Wind Power ◽  
Wind Velocity ◽  
Wind Direction ◽  
Wind Farm ◽  
Farm Size ◽  
Wind Resource

Currently, the quality of wind measure of a site is assessed using Wind Power Density (WPD). This paper proposes to use a more credible metric namely, one we call the Wind Power Potential (WPP). While the former only uses wind speed information, the latter exploits both wind speed and wind direction distributions, and yields more credible estimates. The new measure of quality of a wind resource, the Wind Power Potential Evaluation (WPPE) model, investigates the effect of wind velocity distribution on the optimal net power generation of a farm. Bivariate normal distribution is used to characterize the stochastic variation of wind conditions (speed and direction). The net power generation for a particular farm size and installed capacity are maximized for different distributions of wind speed and wind direction, using the Unrestricted Wind Farm Layout Optimization (UWFLO) methodology. A response surface is constructed, using the recently developed Reliability Based Hybrid Functions (RBHF), to represent the computed maximum power generation as a function of the parameters of the wind velocity (speed and direction) distribution. To this end, for any farm site, we can (i) estimate the parameters of wind velocity distribution using recorded wind data, and (ii) predict the maximum power generation for a specified farm size and capacity, using the developed response surface. The WPPE model is validated through recorded wind data at four differing stations obtained from the North Dakota Agricultural Weather Network (NDAWN). The results illustrate the variation of wind conditions and, subsequently, its influence on the quality of a wind resource.

Wind Power Short-Term Forcasting of BP Neural Network Based on the Small-World Optimization

2014 ◽  
Vol 933 ◽  
pp. 384-389
Author(s):  
Xin Zhao ◽  
Shuang Xin Wang
Keyword(s):  
Neural Network ◽  
Wind Speed ◽  
Wind Power ◽  
Bp Neural Network ◽  
Wind Direction ◽  
Wind Farm ◽  
Prediction Method ◽  
Small World ◽  
Power Method ◽  
Short Term

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%.

scholarly journals Design of Arduino-based small wind power generation system

2018 ◽  
Vol 67 ◽  
pp. 01006 ◽  
Author(s):  
Abdul Goffar Al Mubarok ◽  
Wisnu Djatmiko ◽  
Muhammad Yusro
Keyword(s):  
Power Generation ◽  
Wind Speed ◽  
Wind Energy ◽  
Wind Power ◽  
Wind Direction ◽  
Wind Power Generation ◽  
Generation System ◽  
Remote Location ◽  
Lack Of Information ◽  
Power Generation System

Wind energy is a less-attended renewable energy due to the lack of information about its potential. Some pilot wind turbines were not managed properly and built without considering to the technical feasibility. This study aims to propose a preliminary design of an Arduino-based small wind power generation system. The electricity which is generated by the wind charges the battery. It supplies power of information system which transmits the data of wind speed and wind direction from the remote location to the web server through GPRS network. The remote location which is completed with mobile data coverage is essential for this study. The results of this study are (1) the battery charging stop automatically when the battery is full (2) the data of wind speed and wind direction can be accessed through web browser or Android Smartphone. The data can be used for further analysis to determine the potential of wind energy at the site.

Improving short-term wind power forecasts during ramp events by including nacelle wind speed observations

2021 ◽  
Author(s):  
Moritz Lochmann ◽  
Heike Kalesse-Los ◽  
Michael Schäfer ◽  
Ingrid Heinrich ◽  
Ronny Leinweber
Keyword(s):  
Power Generation ◽  
Wind Speed ◽  
Wind Energy ◽  
Wind Power ◽  
Wind Farm ◽  
Mean Deviation ◽  
Short Term ◽  
Wind Speeds ◽  
Sonic Anemometers ◽  
Ramp Events

<p>Wind energy is and will be one of the key technologies for a transition to green electricity. However, the smooth integration of the generated wind energy into the electrical grid depends on reliable power forecasts. Rapid changes in power generation, so-called ramps, are not always reflected properly in NWP data and pose a challenge for power predictions and, therefore, grid operation. While contributions to the topic of ramp forecasting increased in the recent years, this work approaches the mitigation of deviations from the forecast more directly.</p> <p>The power forecast tool used here is based on an artificial neural network, trained and evaluated on multiple years of data. It is applied in comparison to power generation data for a 44 MW wind farm in Brandenburg. For short-term wind power forecasts, NWP wind speeds in this power forecast tool are replaced with recent Doppler Lidar wind profiles and nacelle wind speed observations from ultra-sonic anemometers, aiming to provide an easy-to-implement way to reduce negative impacts of ramps. Compared to NWP input data, this persistence approach with observational data aims to improve the forecast quality especially during the time of wind ramps.</p> <p>Different ramp definitions and forecast horizons are explored. In general, the number of ramps detected increases dramatically when using wind speed observations instead of the (too smooth) NWP model data. In addition, the mean deviation between power forecast and actual power generation around ramp events decreases, indicating a reduced need for balancing efforts.</p>

scholarly journals The Influence of the Wind Measurement Campaign Duration on a Measure-Correlate-Predict (MCP)-Based Wind Resource Assessment

Energies ◽  
2019 ◽  
Vol 12 (19) ◽  
pp. 3606
Author(s):  
José V. P. Miguel ◽  
Eliane A. Fadigas ◽  
Ildo L. Sauer
Keyword(s):  
Wind Speed ◽  
Wind Power ◽  
Wind Farm ◽  
Target Location ◽  
Resource Assessment ◽  
Measurement Campaign ◽  
Wind Measurement ◽  
Wind Resource Assessment ◽  
Wind Resource

Driven by the energy auctions system, wind power in Brazil is undergoing a phase of expansion within its electric energy mix. Due to wind’s stochastic nature and variability, the wind measurement campaign duration of a wind farm project is required to last for a minimum of 36 months in order for it to partake in energy auctions. In this respect, the influence of such duration on a measure-correlate-predict (MCP) based wind resource assessment was studied to assess the accuracy of generation forecasts. For this purpose, three databases containing time series of wind speed belonging to a site were considered. Campaigns with durations varying from 2 to 6 years were simulated to evaluate the behavior of the uncertainty in the long-term wind resource and to analyze how it impacts a wind farm power output estimation. As the wind measurement campaign length is increased, the uncertainty in the long-term wind resource diminished, thereby reducing the overall uncertainty that pervades the wind power harnessing. Larger monitoring campaigns implied larger quantities of data, thus enabling a better assessment of wind speed variability within that target location. Consequently, the energy production estimation decreased, allowing an improvement in the accuracy of the energy generation prediction by not overestimating it, which could benefit the reliability of the Brazilian electric system.

Modeling Large-Scale Wind Farms for Reliability Analysis Considering Wake Effect

2014 ◽  
Vol 543-547 ◽  
pp. 647-652
Author(s):  
Ye Zhou Hu ◽  
Lin Zhang ◽  
Pai Liu ◽  
Xin Yuan Liu ◽  
Ming Zhou
Keyword(s):  
Power Generation ◽  
Wind Speed ◽  
Wind Power ◽  
Large Scale ◽  
Wind Farm ◽  
Wind Farms ◽  
Wind Power Generation ◽  
Wake Effect ◽  
Effect Model ◽  
The Individual

Large scale wind power penetration has a significant impact on the reliability of the electric generation systems. A wind farm consists of a large number of wind turbine generators (WTGs). A major difficulty in modeling wind farms is that the WTG not have an independent capacity distribution due to the dependence of the individual turbine output on the same energy source, the wind. In this paper, a model of the wind farm output power considering multi-wake effects is established according to the probability distribution of the wind speed and the characteristic of the wind generator output power: based on the simple Jenson wake effect model, the wake effect with wind speed sheer model and the detail wake effect model with the detail shade areas of the upstream wind turbines are discussed respectively. Compared to the individual wake effect model, this model takes the wind farm as a whole and considers the multi-wakes effect on the same unit. As a result the loss of the velocity inside the wind farm is considered more exactly. Furthermore, considering the features of sequentially and self-correlation of wind speed, an auto-regressive and moving average (ARMA) model for wind speed is built up. Also the reliability model of wind farm is built when the output characteristics of wind power generation units, correlation of wind speeds among different wind farms, outage model of wind power generation units, wake effect of wind farm and air temperature are considered. Simulation results validate the effectiveness of the proposed models. These models can be used to research the reliability of power grid containing wind farms, wind farm capacity credit as well as the interconnection among wind farms

Numerical Simulation of Wind Effect Over Industrial Chimneys in Cet West Bucharest

2021 ◽  
Author(s):  
Victorita Radulescu
Keyword(s):  
Numerical Modeling ◽  
Wind Speed ◽  
Velocity Distribution ◽  
Wind Velocity ◽  
Thermal Power ◽  
Cfd Modeling ◽  
Wind Effect ◽  
Real Surface ◽  
Fine Mesh

Abstract The distribution of wind speed in the Atmospheric Boundary Layer - ABL has an essential role in the structural design and modeling of chimneys in thermal power plants. As a case study, the recently rehabilitated West Thermal Power Plant in Bucharest was selected. For the numerical modeling of the wind effect, a database was developed with the atmospheric parameters monitored for more than two years, in the selected area. The known pressure coefficients - Cp for a chimney are only valid for some conventional forms. In the present paper for the numerical modeling of the Cp coefficient and of the wind velocity coefficient, the real surface of the chimney was analyzed, considering also its roughness. A significant effect of the pressure distribution, known as the suction effect, was observed. The vertical distribution of the horizontal component of wind speed is strongly influenced by the presence of nearby buildings. They act as a roughness effect by producing air turbulence, separating the flow and inducing the “wake effect”. This phenomenon produces a variation of the average parameters of wind speed and turbulence, depending on the height and distribution of the buildings. For a proper modeling, some details are mentioned regarding the characteristics and dimensions of the analyzed chimney, associated with the land surface and its topography, with the wind speed and the structure of the chimney. Next, some criteria for modeling and selecting the geometric scale are mentioned, followed by some details on the meshing solution for the CFD modeling. A fine mesh is preferred for the inner and outer surface of the chimney in the bottom area, around the chimney, with a quality of about 0.75 for each model tested. The quality of the element is determined with a determinant of the Jacobian matrix, as a measure of the distortion of the shape of the elements. The inlet profile of dissipation rate ε produced by the turbulence was considered from the approximation of Richards and Hoxey. Knowing the wind velocity distribution and the coefficient of force exerted on the chimney, the acting force of the wind is determined. Some results obtained by numerical modeling are mentioned in the last part of the paper on wind velocity distribution, pressure values and force distribution, as altitude functions. The obtained results are in agreement with the experimental data, the highest difference being of approximately 3.43 %, in the top of the chimney, depending on the margin of the discretization field.

scholarly journals Analysis of Wind Turbine Aging through Operation Data Calibrated by LiDAR Measurement

Energies ◽  
2021 ◽  
Vol 14 (8) ◽  
pp. 2319
Author(s):  
Hyun-Goo Kim ◽  
Jin-Young Kim
Keyword(s):  
Power Generation ◽  
Wind Speed ◽  
Wind Turbine ◽  
Wind Farm ◽  
Free Stream ◽  
Turbine Blades ◽  
Wind Farms ◽  
Lidar Measurement ◽  
Wind Turbine Blades ◽  
Wake Effect

This study analyzed the performance decline of wind turbine with age using the SCADA (Supervisory Control And Data Acquisition) data and the short-term in situ LiDAR (Light Detection and Ranging) measurements taken at the Shinan wind farm located on the coast of Bigeumdo Island in the southwestern sea of South Korea. Existing methods have generally attempted to estimate performance aging through long-term trend analysis of a normalized capacity factor in which wind speed variability is calibrated. However, this study proposes a new method using SCADA data for wind farms whose total operation period is short (less than a decade). That is, the trend of power output deficit between predicted and actual power generation was analyzed in order to estimate performance aging, wherein a theoretically predicted level of power generation was calculated by substituting a free stream wind speed projecting to a wind turbine into its power curve. To calibrate a distorted wind speed measurement in a nacelle anemometer caused by the wake effect resulting from the rotation of wind-turbine blades and the shape of the nacelle, the free stream wind speed was measured using LiDAR remote sensing as the reference data; and the nacelle transfer function, which converts nacelle wind speed into free stream wind speed, was derived. A four-year analysis of the Shinan wind farm showed that the rate of performance aging of the wind turbines was estimated to be −0.52%p/year.

Hub height optimisation of commercial WTGs based on accurate wind resource analysis

2021 ◽  
pp. 0309524X2110227
Author(s):  
Kyle O Roberts ◽  
Nawaz Mahomed
Keyword(s):  
Power Generation ◽  
Wind Turbine ◽  
Wind Power ◽  
Capacity Factor ◽  
Resource Analysis ◽  
Wind Speeds ◽  
Unique Maximum ◽  
Wind Measurements ◽  
Generation Capacity ◽  
Wind Resource

Wind turbine selection and optimal hub height positioning are crucial elements of wind power projects. However, in higher class wind speeds especially, over-exposure of wind turbines can lead to a reduction in power generation capacity. In this study, wind measurements from a met mast were validated according to specifications issued by IRENA and NREL. As a first step, it is shown that commercial WTGs from a database may be matched to the wind class and turbulence intensity. Secondly, a wind turbine selection algorithm, based on maximisation of capacity factor, was implemented across the range of WTGs. The selected WTGs were further exposed to an iterative algorithm using pointwise air density and wind shear coefficients. It is shown that a unique maximum capacity factor, and hence wind power generation, exists for a wind turbine, premised on its eventual over-exposure to the wind resource above a certain hub height.

scholarly journals A Reliability Based Model for Wind Turbine Selection

2013 ◽  
Vol 2 (2) ◽  
pp. 69-74 ◽  
Author(s):  
A.K. Rajeevan ◽  
P.V. Shouri ◽  
Usha Nair
Keyword(s):  
Power Generation ◽  
Wind Speed ◽  
Wind Turbine ◽  
Wind Power ◽  
Point Of View ◽  
Cube Root ◽  
Mathematical Relationship ◽  
Turbine Generator ◽  
Wind Turbine Generator ◽  
A Site

A wind turbine generator output at a specific site depends on many factors, particularly cut- in, rated and cut-out wind speed parameters. Hence power output varies from turbine to turbine. The objective of this paper is to develop a mathematical relationship between reliability and wind power generation. The analytical computation of monthly wind power is obtained from weibull statistical model using cubic mean cube root of wind speed. Reliability calculation is based on failure probability analysis. There are many different types of wind turbinescommercially available in the market. From reliability point of view, to get optimum reliability in power generation, it is desirable to select a wind turbine generator which is best suited for a site. The mathematical relationship developed in this paper can be used for site-matching turbine selection in reliability point of view.

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