Wind Speed Frequency Distribution Research Articles

Technical and economic evaluation of energy production from wind in Istanbul and surrounds

Istanbul is located in the northwest part of Turkey and has the greatest population of any city in the country. Istanbul and its surroundings were researched for wind potential. Energy production from wind was evaluated technically and economically in this study. Çorlu, Kumköy and Şile sites were examined. Other sites in the same region (Göztepe, Kireçburnu, Bahçeköy, Florya and Kartal) were also evaluated but the results were not satisfactory, and for this reason, these sites were not examined in this study. When we checked daily, monthly and yearly wind speed values and frequency distributions, the wind energy potential of Çorlu, Kumköy and Şile were greater than other sites. Çorlu, Kumköy and especially Şile’s wind speed data are statistically decreasing. A Nordex N117 91m hub height wind turbine can produce 6099 MWh in Çorlu, 6459 MWh in Şile, 7265 MWh in Kumköy. A Nordex N117 140 m hub height wind turbine can produce 6471 MWh in Çorlu, 7439 MWh in Şile and 8175 MWh in Kumköy. The energy costs were calculated by the average of 36 years of wind measurement data using Nordex N117 turbines. Energy costs with Nordex N117 91 m and 140 m hub height in Çorlu, Şile and Kumköy are 0.025-0,027 US$/KWh, 0.022-0,025 US$/KWh, 0.020-0,023 US$/KWh, respectively.

Technical and economic evaluation of energy production from wind in Istanbul and surrounds

Istanbul is located in the northwest part of Turkey and has the greatest population of any city in the country. Istanbul and its surroundings were researched for wind potential. Energy production from wind was evaluated technically and economically in this study. Corlu, Kumkoy and Sile sites were examined. Other sites in the same region (Goztepe, Kirecburnu, Bahcekoy, Florya and Kartal) were also evaluated but the results were not satisfactory, and for this reason, these sites were not examined in this study. When we checked daily, monthly and yearly wind speed values and frequency distributions, the wind energy potential of Corlu, Kumkoy and Sile were greater than other sites. Corlu, Kumkoy and especially Sile’s wind speed data are statistically decreasing. A Nordex N117 91m hub height wind turbine can produce 6099 MWh in Corlu, 6459 MWh in Sile, 7265 MWh in Kumkoy. A Nordex N117 140 m hub height wind turbine can produce 6471 MWh in Corlu, 7439 MWh in Sile and 8175 MWh in Kumkoy. The energy costs were calculated by the average of 36 years of wind measurement data using Nordex N117 turbines. Energy costs with Nordex N117 91 m and 140 m hub height in Corlu, Sile and Kumkoy are 0.025-0,027 US$/KWh, 0.022-0,025 US$/KWh, 0.020-0,023 US$/KWh, respectively.

Climate trends on the extreme winds in Brazil

The main source of electricity in Brazil is from hydro, which has about 65.2% share of the country’s electric energy matrix. However, over the last decade the wind energy increased from 19 MW to 2.2 GW. Since wind is an intermittent energy source, heavily determined by the weather and climatic conditions, and important effects on wind power generation can be expected in the mid and long term, in particular related to the impacts of extreme winds. The IPCC AR5 (Intergovernmental Panel on Climate Change) indicates changes in wind speed at the surface in some regions of the world, and increased wind strength in mid-latitude regions. This study scrutinizes future scenarios of extreme winds in Brazil by applying trend analysis techniques on a 50-year historical series of observational wind speed and meteorological parameters at 10 m height in Brazil. Embracing techniques of cluster analysis it was possible to characterize six main regions with macro climatic similarities. To assess the goodness fit distribution, we designate two stations per homogenous region, taking as criteria the stations with better performance in the qualification process to determine the wind distribution pattern in each region applying the Kolmogorov-Smirnov test (KS) and the lowest standard error (SE). After evaluating the frequency distribution of wind speed, the best fit result for the frequency distribution of maximum wind speed is the Gumbel model. The analysis of climatic trends performed by Mann-Kendall test revealed that in minimum wind speed series is not conclusive because it shows disparate results between homogeneous regions. On the other hand, the analysis of climatic trends of maximum wind speed presents 100% positive trends in Group#1, an equal number of stations with not significant trends and positive trends for Group#2, 36.8% more stations with positive trends than negative trends for Group#3 and 20% of stations with more negative trends than stations with positive trends for Group#4. This way, based in these results, is possible assert that there are an increase in the maximum extreme wind in Brazil, mainly in mid-latitudes.

Atmospheric air density analysis with Meteo-40S wind monitoring system

In order to estimate the wind potential of wind turbine sites, the wind resource maps can be used for mean annual wind speed, wind speed frequency distribution and mean annual wind power density determination. The general evaluation of the wind resource and the wind turbine ratings are based on the standard air density measured at sea level and at 15°C, ρ s =1.225 kg/m 3 . Based on the experimental data obtained for a continental climate specific location, this study will present the relative error between the standard air density and the density of the dry and the moist air. Considering a cold day, for example on Friday 10 th February 2017, on 1-second measurement rate and 10-minute measuring interval starting at 16:20, the mean relative errors obtained are 10.4145% for dry air, and 10.3634% for moist air. Based on these results, a correction for temperature, atmospheric air pressure and relative humidity should be always considered for wind resource assessment, as well as for the predicting the wind turbines performance.

Wind speed analysis and energy calculation based on mixture distributions in Narakkalliya, Sri Lanka

Wind is an important resource for generating renewable energy. To effectively estimate wind energy potential, Weibull, lognormal and gamma distribution functions have been widely used. However, when the distribution is bi-modal these conventional distribution functions are not suitable. Since Narakkalliya is affected by both monsoon seasons to varying degrees, the wind speed data show a bi-modal behaviour. A study was conducted at Narakkalliya in 2001 to calculate the energy generated from wind using the conventional Weibull distribution. The objective of this study was to show that energy calculation could be carried out more accurately using mixture distribution. Thus in this study, mixture Weibull distributions, mixture lognormal and Weibull distribution, mixture gamma and Weibull distribution were considered to model the wind speed frequency distribution (WSFD). Parameters of the above mentioned distributions were calculated using maximum likelihood method. The goodness-of-fit of the distributions was compared using K-S error, Chi-square test and root mean square error. Mixture Weibull distribution had the lowest error followed by the mixture lognormal and Weibull distribution. The fitted mixture Weibull distribution and the power curve data for the Narakkalliya wind turbine were used for energy calculation. According to the analysis mixture Weibull estimates 83.2 % of the actual energy while Weibull estimates 75.9 % of the actual.

The effect of wakes on the fatigue damage of wind turbine components over their entire lifetime using short-term load measurements

A method is developed for quantifying the effect of neighboring wind turbines on the fatigue damage of the main components of a wind turbine over its entire operating time using short-term load measurements. This method could be used in the future for improving wind farm planning software that takes into account fatigue damage as well as energy yield or for improving lifetime extension calculations of wind turbines. The method is applied here to a measurement campaign on a Vestas V66 wind turbine located in northern Germany and the results are found to be plausible. Furthermore, the results show that the increase in total lifetime fatigue damage due to neighboring wind turbines for wind turbine separations of the order of 5D is significant and needs to be taken account of in wind farm planning software. The accuracy of the method is examined by investigating the sensitivity of the main assumptions on the results. It is found to be strongly dependent on the number of measured time-series in a wind speed bin as well as on the choice of wind speed frequency distribution. The method therefore needs to be standardized before it is applied to improving wind farm planning software or lifetime extension calculations of wind turbines.

Reconstructing long-term wind data at an offshore met-mast location using cyclostationary empirical orthogonal functions

For the development of a wind power plant, plant design and its project feasibility analysis are implemented with wind data observed by a met-mast at a target location. Since observation period/time of a met-mast is normally about one year before the plant design, correlation with long-term data existing in the neighborhood of the target location is used for hindcasting past met-mast data to reduce uncertainty in the feasibility analysis, which is called the Measure-Correlate-Predict (MCP) method. In this study, cyclostationary empirical orthogonal function (CSEOF) analysis as a new approach is employed to extend the 1.5-year offshore met-mast HeMOSU-1 data into 34-year long-term data based on the MERRA reanalysis dataset. Both the one- and two-dimensional CSEOF results are compared with that of the widely-used MCP method. The CSEOF method shows a similar level of accuracy to the existing method for mean wind speed, while the former exhibits a slightly better accuracy for the frequency distribution of wind speed and the capacity factor as an index related to the estimation of wind power generation. In additional hypothetical test based on reanalysis datasets, the 1D-CSEOF method shows, in general, a better performance than the conventional MCP method in terms of the accuracy of statistical properties of wind.

On estimating uncertainty of wind energy with mixture of distributions

Accurate estimation of wind speed frequency distribution will be beneficial to predict accurately wind energy potential and select optimal wind energy conversion systems. Due to the complexity of wind regime, it has been reported that mixture models, constituted by multiple distributions (components), perform better than single models, which are commonly used to model wind speed frequency distribution. The underlying assumption in the existing mixture models is that each component is a unimodal distribution. In this work, we propose a more general model, named hierarchical mixture of multiple distributions. To deal with the bimodal or multimodal case in components, we assume that each component may also be a mixture model. In order to estimate the parameters in the proposed model, a nested expectation maximization algorithm is introduced. The experimental results indicate that, the proposed model outperforms single and mixture models. Moreover, due to the complex expression of the probability density function derived by the proposed model, we also develop a technique to estimate wind energy potential. Accordingly, the performances of different wind energy conversion systems are evaluated via four indicators, which provide comprehensive basis for selecting optimal wind energy conversion system.

Statistical analysis of wind speed using two-parameter Weibull distribution in Alaçatı region

Weibull Statistical Distribution is a common method for analyzing wind speed measurements and determining wind energy potential. Weibull probability density function can be used to forecast wind speed, wind density and wind energy potential. In this study a two-parameter Weibull statistical distribution is used to analyze the wind characteristics of Alaçatı region, located in Çeşme, İzmir. The data used in the density function are acquired from a wind measurement station in Alaçatı. Measurements were gathered on three different heights respectively 70, 50 and 30m between 10min intervals for five and half years. As a result of this study; wind speed frequency distribution, wind direction trends, mean wind speed, and the shape and the scale (k&c) Weibull parameters have been calculated for the region. Mean wind speed for the entirety of the data set is found to be 8.11m/s. k&c parameters are found as 2.05 and 9.16 in relative order. Wind direction analysis along with a wind rose graph for the region is also provided with the study. Analysis suggests that higher wind speeds which range from 6–12m/s are prevalent between the sectors 340–360°. Lower wind speeds, from 3 to 6m/s occur between sectors 10–29°. Results of this study contribute to the general knowledge about the regions wind energy potential and can be used as a source for investors and academics.

Determine Wind Frequency Distributions Through the Surface Layer of Baghdad City

This study is made for determining wind speed frequency distributions at any height within Surface Layer SL (SL has height about 250m above ground) ,where wind speed is measured at low reference level (usually 10m) which is considered a routine meteorological data recorded hourly. This study needs to know in detail the roughness characteristics of the terrain surrounding the location study, that important to calculate the rate of growth of internal boundary layers resulting from discontinuities in roughness as well as the shape of wind profile in various layers. The shape characteristics of the wind profile are determined as a function of roughness length and stability and the result of this profile is compared with measurements from radiosonde, at height 10, 50,100,150, 200m. This study tested frequency percent for wind speed at 10m, 50m, 200m for the observed and calculated at July– month 1990 and at the 12:00 hour only. The calculated wind frequency has weak at 10m wind speed and all the frequency is concentrated at the (2-6) m/s, while the observed has high frequency at the wind speed reached to 8m/s. This case is changed at the height 50m, and 200m where the calculated have high frequency at the wind speed concentrated at (6-10) m/s. This study refers to the possibility of installing turbine in this layer to generate electricity at least in this period of this year.

PM10 Dispersion in Adelaide and Its Relationship with Rainfall

The aim of this study is to use a range of statistical tools to assess particulate matter less than 10 μm (PM10) in the atmosphere that has been measured daily at five locations in South Australia over a 7-year period. We consider a wind rose model to provide a graphical display of the frequency distribution of wind speed to explore the role of PM10 accumulation over time. A generalised least squares technique with a first-order autoregressive model was applied to the realisation of average changes in PM10, and these were assessed at the 5 % significance level. This study found the change in variability of PM10 concentration over time. The pre-whitened PM10 series were considered as realisations of white noise using correlogram plots. Furthermore, a robust regression technique involving wet (>0.5-mm rainfall) and dry properties (<0.5-mm rainfall) was used to assess the influence of rainfall on PM10 distributions for the city of Adelaide.

Comparison of regional and global reanalysis near-surface winds with station observations over Germany

Abstract. Reanalysis near-surface wind fields from multiple reanalyses are potentially an important information source for wind energy applications. Inter-comparing reanalyses via employing independent observations can help to guide users to useful spatio-temporal scales. Here we compare the statistical properties of wind speeds observed at 210 traditional meteorological stations over Germany with the reanalyses' near-surface fields, confining the analysis to the recent years (2007 to 2010). In this period, the station time series in Germany can be expected to be mostly homogeneous. We compare with a regional reanalysis (COSMO-REA6) and two global reanalyses, ERA-Interim and ERA-20C. We show that for the majority of the stations, the Weibull parameters of the daily mean wind speed frequency distribution match remarkably well with the ones derived from the reanalysis fields. High correlations (larger than 0.9) can be found between stations and reanalysis monthly mean wind speeds all over Germany. Generally, the correlation between the higher resolved COSMO-REA6 wind fields and station observations is highest, for both assimilated and non-assimilated (i.e., independent) observations. As expected from the lower spatial resolution and reduced amount of data assimilated into ERA-20C, the correlation of monthly means decreases somewhat relative to the other reanalyses (in our investigated period of 2007 to 2010). Still, the inter-annual variability connected to the North Atlantic Oscillation (NAO) found in the reanalysis surface wind anomalies is in accordance with the anomalies recorded by the stations. We discuss some typical examples where differences are found, e.g., where the mean wind distributions differ (probably related to either height or model topography differences) and where the correlations break down (because of unresolved local topography) which applies to a minority of stations. We also identified stations with homogeneity problems in the reported station values, demonstrating how reanalyses can be applied to support quality control for the observed station data. Finally, as a demonstration of concept, we discuss how comparing feedback files of the different reanalyses can guide users to useful scales of variability.

Adaptive neuro-fuzzy approach for estimation of wind speed distribution

Abstract Probability distribution of wind speed is very important information needed in the assessment of wind energy potential. For this reason, a large number of studies have been published concerning the use of a variety of probability density functions to describe wind speed frequency distributions. Two parameter Weibull distribution is widely used and accepted method. In this investigation adaptive neuro-fuzzy inference system (ANFIS) was used to predict the probability density distribution of wind speed. The estimation and prediction results of ANFIS model are calculated using three statistical indicators i.e. root means square error, coefficient of determination and Pearson coefficient. The results show that an improvement in predictive accuracy and capability of generalization can be achieved by the ANFIS approach. Moreover, the results indicate that proposed ANFIS model can adequately predict the probability distribution of wind speed.

Assessment of wind energy potential in East Azarbaijan province of Iran (Case Stady: Sahand station)

&lt;p&gt;Today&amp;#39;s world requires a change in how the use of different types of energy. With declining reserves of fossil fuels for renewable energies is of course the best alternative. Among the renewable energy from the wind can be considered one of the best forms of energy can be introduced. Accordingly, most countries are trying to identify areas with potential to benefit from this resource.&lt;/p&gt; &lt;p&gt;The aim of this study was to assess the potential wind power in Sahand station of Iran country. Hourly measured long term wind speed data of Sahand during the period of 2000-2013 have been statistically analyzed. In this study the wind speed frequency distribution of location was found by using Weibull distribution function. The wind energy potential of the location has been studied based on the Weibull mode. The results of this study show that mean wind speed measured at 10 m above ground level is determined as 5.16 m/s for the studied period. This speed increases by, respectively, 34.78 % and 41.21 %, when it is extrapolated to 40 and 60 m hub height.&lt;/p&gt; &lt;div&gt; &lt;p&gt;Long term seasonal wind speeds were found to be relatively higher during the period from January to September. At the other hand, higher wind speeds were observed between the period between 06:00 and 18:00 in the day. These periods feet well with annual and daily periods of maximum demand of electricity, respectively.&amp;nbsp;&lt;/p&gt; &lt;/div&gt; &lt;p&gt;&amp;nbsp;&lt;/p&gt;

RETRACTED: Generalized adaptive neuro-fuzzy based method for wind speed distribution prediction

The probabilistic distribution of wind speed is one of the important wind characteristics for the assessment of wind energy potential and for the performance of wind energy conversion systems. When the wind speed probability distribution is known, the wind energy distribution can easily be obtained. Therefore, the probability distribution of wind speed is a very important piece of information needed in the assessment of wind energy potential. For this reason, a large number of studies have been published concerning the use of a variety of probability density functions to describe wind speed frequency distributions. Two parameter Weibull distribution is widely used and accepted method. Artificial neural networks (ANN) can be used as an alternative to analytical approach as ANN offers advantages such as no required knowledge of internal system parameters, compact solution for multi-variable problems. In this investigation adaptive neuro-fuzzy inference system (ANFIS), which is a specific type of the ANN family, was used to predict the annual probability density distribution of wind speed. The simulation results presented in this paper show the effectiveness of the developed method.

Mapping wind power density for Zimbabwe: a suitable Weibull-parameter calculation method

The two-parameter Weibull probability distribution function is versatile for modelling wind speed frequency distribution and for estimating the energy delivery potential of wind energy systems if its shape and scale parameters, k and c, are correctly determined from wind records. In this study, different methods for determining Weibull k and c from wind speed measurements are reviewed and applied at four sample meteorological stations in Zimbabwe. The appropriateness of each method in modelling the wind data is appraised by its accuracy in predicting the power density using relative deviation and normalised root mean square error. From the methods considered, the graphical method proved to imitate the wind data most closely followed by the standard deviation method. The Rayleigh distribution (k=2 is also generated and compared with the wind speed data. The Weibull parameters were calculated by the graphical method for fourteen stations at which hourly wind speed data was available. These values were then used, with the assistance of appropriate boundary layer models, in the mapping of a wind power density map at 50m hub height for Zimbabwe.

Mixed Weibull Distribution: A Case Study on Ichanda, India

This paper presents a case study of a new methodology to accurately characterize and predict the annual variation of wind conditions applied on Ichanda site, Tamil Nadu, India showing dual behaviour. The estimate of the distribution of wind conditions is necessary to quantify the available energy (power density) at a site, and to design an optimum wind farm. Wind speed frequency distribution for some sites with two distinct peaks is not represented accurately by the typical two parameter Weibull distribution. The wind characteristics of Ichanda has been analysed by using wind data recorded by meteorological mast installed at that location. By examining the analysis, it shows that wind speed distribution is not demonstrated precisely by two parameter Weibull distribution. A mixed Weibull probability distribution function (PDF) is applied to analyse wind speed frequency distribution in that region. This model can be applied to regions where the wind speed distribution presents a bimodal distribution to predict wind speed probability distribution and annual energy production accurately.

Investigating Wind Resource, Turbulence Intensity, and Gust Factor on Mountain Locations in Southern Italy

Wind energy potential and turbulence characteristics on four mountain locations in Southern Italy have been analyzed based on a 2 year wind dataset composed of 10 min averaged records collected at 30 m. These sites have been identified as eligible areas to be exploited, being representative of the typical mid-altitude Apennine rural ridges running the length of the country, as well as quite easily accessed and free from any environmental constraint. Wind resource has been assessed through wind roses, wind speed frequency distributions, and Weibull probability density function. Turbulence intensity and gust factor variation by month, hour of day, and wind direction has been also assessed. In addition, the Counihan and Wieringa models to calculate turbulence intensity and gust factor, respectively, have been implemented and compared to observations.

Wind resource estimates with an analog ensemble approach

The wind resource and energy assessment is key to a wind farm development project. It allows for establishing the feasibility and economic viability of the project over the typical 10- to 30-year lifetime of a wind farm. Recent studies show that the accuracy of assessments has substantial room for improvement. Estimating and reducing uncertainty is important to secure financing and ensure the confidence of investors. A new method is proposed and demonstrated for the long-term estimation of the wind speeds at a target site, a key step in assessments. The method is based on ensembles made of analogs between a short-term observational record from the target site and a long-term historical record from a nearby site or an atmospheric model. It provides a high-quality long-term wind resource estimate, characterized by an accurate wind speed time series and frequency distribution. It also provides a reliable estimate of the uncertainty based on the actual physical processes determining the current atmospheric flow rather than the climatological wind distribution.

RETRACTED ARTICLE: Adaptive neuro-fuzzy evaluation of wind farm power production as function of wind speed and direction

Wind velocity assumes a critical part for measuring the power created by the wind turbines. Nonetheless, power production from wind has a few weaknesses. One significant issue is that wind is a discontinuous energy source which implies that there exists substantial variability in the generation of vigor because of different variables, for example, wind speed. Wind direction is a significant variable for proficient turbine control for getting the most energy with a given wind speed. Taking into account the conjectures on wind heading, it might be conceivable to adjust the turbine to the wind bearing to get the most energy yield. Since both forecasts of wind speed and direction are basic for effective wind energy collecting it is crucial to develop a methodology for estimation of wind speed and direction and afterwards to estimate wind farm power production as function of wind pace and heading distribution. Despite the fact that various numerical functions have been proposed for demonstrating the wind speed and direction frequency distribution, there are still disadvantages of the models like very demanding in terms of calculation time. In this investigation adaptive neuro-fuzzy inference system (ANFIS), which is a particular sort of the artificial neural networks (ANN) family, was used to anticipate the wind speed and direction frequency dispersion. Thereafter, the ANFIS system was utilized to gauge wind homestead power creation as function of wind velocity and bearing. Neural system in ANFIS modifies parameters of enrollment capacity in the fuzzy logic of the fuzzy inference system. The reenactment outcomes exhibited in this paper demonstrate the adequacy of the created technique.