Location wise comparison of mixture distributions for assessment of wind power potential: A parametric study

Scientific literature discussed various types of mixture models and models derived from maximum entropy principle using short-term wind speed data for their relative assessment. The literature on suitability of these mixture models for long-term data is rarely available. However, for correct assessment of wind power potential both wind speed and wind direction are equally important. Therefore, in this paper, both wind speed and wind direction are simultaneously analyzed using several types of mixture distribution and compared the same with conventional Weibull distribution. For wind speed and wind power density assessment, the mixture distributions such as Weibull--Weibull distribution, Gamma--Weibull distribution, Truncated Normal--Weibull distribution, Truncated Normal--Normal distribution, proposed Truncated Normal--Gamma distribution and Gamma--Gamma distribution along with MEP-distribution are compared with conventional 2-parameter Weibull distribution. Similarly, for wind direction analysis, the finite mixtures of von-Mises distribution are compared with conventional von-Mises distribution. Judgment criteria include R², RMSE, Kolmogorov--Smirnov test and relative percentage error in wind power density. The sites selected are the three onshore locations of India, viz., Calcutta, Trivandrum, and Ahmedabad. The results show that for wind speed assessment, mixture distribution performs better than the conventional Weibull distribution for analyzing wind power density. However, location wise comparison of all mixture distribution is of prime importance. For wind direction analysis, finite mixture of two von-Mises distributions proved to be a suitable candidate for Indian climatology.     

A Weibull and finite mixture of the von Mises distribution for wind analysis in Mersing,Malaysia

Studies of wind direction receive less attention than that of wind speed; however, wind direction affects daily activities such as shipping, the use of bridges, and construction. This research aims to study the effect of wind direction on generating wind power. A finite mixture model of the von Mises distribution and Weibull distribution are used in this paper to represent wind direction and wind speed data, respectively, for Mersing (Malaysia). The suitability of the distribution is examined by the R² determination coefficient. The energy analysis, that is, wind power density, only involves the wind speed, but the wind direction is vital in measuring the dominant direction of wind so that the sensor could optimize wind capture. The result reveals that the estimated wind power density is between 18.2 and 25 W/m², and SSW is the most common wind direction for this data.     

An Analysis of the Wind Energy Potential of Elazig,Turkey

Abstract

In this study, the wind energy potential of Elazig is statistically analyzed based on hourly measured wind speed data over the five-year period from 1998 to 2002. The probability density distributions are derived from cumulative distribution functions. Two probability density functions are fitted to the measured probability distribution on a yearly basis. The wind energy potential of the location is studied based on the Weibull and Rayleigh distributions. It was found that the numerical values of both Weibull parameters (k and c) for Elazig vary over a wide range. The yearly values of k range from 1.653 to 1.878 with an average value of 1.819, while those of c are in the range of 2.757–2.994 m/s with an average value of 2.824 m/s. In addition, yearly mean wind speed and mean power density of Elazig is found as 2.79 m/s and 38.76 W/m², respectively. The wind speed distributions are represented by Weibull distribution and also by Rayleigh distribution, with a special case of the Weibull distribution for k = 2. As a result, the Rayleigh distribution is found to be suitable to represent the actual probability of wind speed data for Elazig.     

Wind energy potential assessment and techno-economic performance of wind turbines in coastal sites of Buenos Aires province,Argentina

An assessment of wind energy potential was carried out in five sites (four onshore and one offshore) in South-West (SW) of Buenos Aires province (Argentina). We use high-resolution wind data (2 and 5 min) for the period 2009–2012. The power law was used to estimate the wind speed at 30, 40, and 60 m height from the anemometer position. Turbulence intensity and wind direction were analyzed. Statistical analyses were conducted using two-parameter Weibull distribution. A techno-economic analysis based on a set of commercial wind turbines was performed in those sites. The results derived from this work indicate that the SW of Buenos Aires province represents a promising area for the wind energy extraction, which would encourage the construction of wind farms for electricity generation.     

Suitable error evaluation criteria selection in the wind energy assessment via the K-means clustering algorithm

In this paper, wind energy potential of four locations in Xinjiang region is assessed. The Weibull distribution as well as the Logistic and the Lognormal distributions are applied to describe the distributions of the wind speed at different heights. In determining the parameters in the Weibull distribution, four intelligent parameter optimization approaches including the differential evolutionary, the particle swarm optimization, and two other approaches derived from these two algorithms and combined advantages of these two approaches are employed. Then the optimal distribution is chosen through the Chi-square error (CSE), the Kolmogorov–Smirnov test error (KSE), and the root mean square error (RMSE) criteria. However, it is found that the variation range of some criteria is quite large, thus these criteria are analyzed and evaluated both from the anomalous values and by the K-means clustering method. Anomaly observation results have shown that the CSE is the first one should be considered to be eliminated from the consequent optimal distribution function selection. This idea is further confirmed by the K-means clustering algorithm, by which the CSE is clustered into a different group with KSE and RMSE. Therefore, only the reserved two error evaluation criteria are utilized to evaluate the wind power potential.     

A hybrid model based on smooth transition periodic autoregressive and Elman artificial neural network for wind speed forecasting of the Hebei region in China

Wind energy, one of the most promising renewable and clean energy sources, is becoming increasingly significant for sustainable energy development and environmental protection. Given the relationship between wind power and wind speed, precise prediction of wind speed for wind energy estimation and wind power generation is important. For proper and efficient evaluation of wind speed, a smooth transition periodic autoregressive (STPAR) model is developed to predict the six-hourly wind speeds. In addition, the Elman artificial neural network (EANN)-based error correction technique has also been integrated into the new STPAR model to improve model performance. To verify the developed approach, the six-hourly wind speed series during the period of 2000–2009 in the Hebei region of China is used for model construction and model testing. The proposed EANN-STPAR hybrid model has demonstrated its powerful forecasting capacity for wind speed series with complicated characteristics of linearity, seasonality and nonlinearity, which indicates that the proposed hybrid model is notably efficient and practical for wind speed forecasting, especially for the Hebei wind farms of China.     

Short-term wind speed forecasting: Application of linear and non-linear time series models

This study forecasts day-ahead wind speed at 15 minute intervals at the site of a wind turbine located in Maharashtra, India. Wind speed exhibits non-stationarity, seasonality and time-varying volatility clustering. Univariate linear and non-linear time series techniques namely MSARIMA, MSARIMA-GARCH and MSARIMA-EGARCH have been employed for forecasting wind speed using data span ranging from 3 days to 15 days. Study suggests that mean absolute percentage error (MAPE) values first decrease with the increase in data span, reaches its minima and then start increasing. All models provide superior forecasting performances with 5 days data span. It is further evident that ARIMA-GARCH model generates lowest MAPE with 5 days data span. All these models provide superior forecasts with respect to current industry practices. This study establishes that employing various linear and non-linear time series techniques for forecasting day-ahead wind speed can benefit the industry in terms of better operational management of wind turbines and better integration of wind energy into the power system, which have huge financial implications for wind power generators in India.     

Performance evaluation of a horizontal axis wind turbine in operation

The operation of modern horizontal axis wind turbine (HAWT) includes a number of important factors, such as wind power (P), power coefficient (C_P), axial flow induction factor (a), rotational speed (Ω), tip speed ratio (λ), and thrust force (T). The aerodynamic qualities of these aspects are evaluated and discussed in this study. For this aim, the measured data are obtained from the Sebenoba Wind Energy Power Plant (WEPP) that is located in the Sebenoba region in Hatay, Turkey, and a wind turbine with a capacity of 2 MW is selected for evaluation. According to the results obtained, the maximum turbine power output, maximum power coefficient, maximum axial flow induction factor, maximum thrust force, optimum rotational speed, probability density of optimum rotational speed, and optimum tip speed ratio are found to be 2 MW, 30%, 0.091, 140 kN, 16.11 rpm, 46.76%, and 7, respectively. This study has revealed that wind turbines must work under optimum conditions in order to extract as much energy as possible for approaching the ideal limit.     

A stochastic approach for the operation of a wind and pumped storage plant under a deregulated environment

To improve the competiveness in the energy market, it is necessary that the wind power plants provide guaranteed power generation, although, it is not possible to forecast power availability from wind power plant accurately. This paper presents a stochastic model and solution technique for the combined operation of wind and pumped storage power plants to improve the power availability and increasing the profit considering uncertainties of wind power generation. In this model, uncertainties in wind data have been forecasted for grid connected day-ahead market using Weibull distribution model. The imbalances in the forecasted wind data and the market demand have been reduced by operating the pumped storage power plant. In this stochastic mixed integer problem, pumped storage plant can take the supply either from the grid or from the wind power plant for the pumping operation to store the energy in order to utilize this energy during peak hours for increasing the overall revenue. The reliability of the pumped storage is improved by replacing the conventional unit with the adjustable speed type pumped storage unit. In order to prove the optimality of the solution, two case studies were considered. In case study–I, scheduling is provided by operating the conventional pumped storage unit, whereas in case study–II, adjustable speed pumped storage unit has been used. It has been found that the adjustable speed pumped storage unit has further reduced the imbalance between generated power and demand. The complete approach has been formulated and implemented using AMPL software.     

A novel blade design for Savonius wind turbine based on polynomial bezier curves for aerodynamic performance enhancement

ABSTRACT

Advanced wind turbine designs and technologies have been evolved to take advantage of wind energy. Despite the significant progress already attained, the need for a dependable wind energy converter particularly devoted to small-scale applications remains a challenging issue. Due to its design simplicity, Savonius wind turbine is the most suitable candidate for such applications. It operates at low wind speed, with the necessary starting capacity and insensitivity to wind directions. Moreover, in the literature related to wind energy, the Savonius rotor is known for its low performance compared to other types of wind turbines. In this paper, we present a study into the utilization of Bézier curves and transient computational fluid dynamics (CFD) to optimize the conventional Savonius blade design. The k-ω SST turbulence model is employed to perform a series of CFD simulations in order to assess the power coefficient of each generated design. A validation of optimization results using the Taguchi method was carried out. The comparative analysis of the torque and power coefficients shows a significant increase in the power coefficient (C_p). The optimal C_p is 0.35 and is 29% higher than the conventional Savoniu wind turbine (SWT). Subsequently, the effectiveness of the innovative geometry is proved by improved pressure and velocity distributions around blades of novel design.     

A wind-solar PV hybrid power system with battery backup for water pumping in remote localities

After energy, water is the most critical commodity to be made available to people to keep them alive. Saudi Arabia has vast land and people are living in all regions. Most of these are connected to national grid but some are not, especially in remote areas like in the north, south, and west south. Pumping water in remote areas for domestic needs like agriculture and animals beside human needs is essential and require regular power supply. The present idea of wind-PV-Battery hybrid power system based on 100% renewable source is being proposed to utilize and tested in some of the regions on experimental bases. Of the five locations chosen for the purpose, namely Dhahran, Riyadh, Jeddah, Guriat and Nejran, some are good from both wind and solar intensity point of view some have good winds only and some good solar only. Nearly optimal size of PV-Wind water pumping system is determined for each of these sites considering the availability of solar and wind energy distributions throughout the year in these sites. It is shown that the monthly total water pumping capacity when using nearly optimal PV-Wind water pumping system is fairly uniform throughout the year except for the sites of Guriat and Riyadh. In these sites higher water pumping capacity is observed during the spring and summer months. On the other hand the cost of underground water pumping is found to vary between 6 to 12 US¢/m³ for the five sites considered.     

成都市温江边界层风场特征的研究

着眼于成都地区污染气象特征,利用温江2004年~2012年的地面观测资料及同期的探空资料,对该地区风场的统计特征进行了分析。结果表明:(1)从风频而言,温江地面风春、夏、秋3季均以偏北风为主,冬季则以东北风为主,因此,全年的主导风为偏北风。(2)从风速而言,区域静风和小风频率较高,占全年的68.7%;近10年来,温江年和4季的平均风速均呈现出减小趋势。(3)基于修正的帕斯奎尔稳定度分级法,利用幂指数律公式拟合了风随高度的变化,发现风廓线指数比国标值偏高,并随稳定度的增加而增大。(4)风速、稳定度联合频率的大值区主要出现在风速小于3m/s、稳定度为D~E类。上述研究成果对区域大气环境规划和工程治理具有重要的参考价值。     

A convective wind resource model for Solar Vortex power generation

ABSTRACT

Climate change has increased the need for clean, nonpolluting energy sources to decrease dependence on fossil fuels. Alternative energy sources, mainly solar and horizontal wind, have been the primary focus for producing clean energy. New technologies are being developed, such as the Solar Vortex (SoV), which was developed at the Georgia Institute of Technology, and relies on a vertical wind resource to generate power. The National Renewable Energy Lab (NREL) has resource models representing solar and horizontal wind resources across the 48 United States. This research developed a vertical wind resource model that is comparable in resolution to NREL’s solar and horizontal wind resource models and uses the model for estimating power output for the SoV. This model complements NREL’s existing resource models and supports the deployment of an additional clean energy generation technology. The model was applied to Mesa, Arizona to find feasible sites for a small-scale vertical wind farm.     

Nonlinear control of a wind turbine based on nonlinear estimation techniques for maximum power extraction

This work proposes nonlinear estimators with nonlinear controllers, for variable speed wind turbine (VSWT) considering that either the wind speed measurement is not available or not accurate. The main objective of this work is to maximize the energy capture from the wind and minimizes the transient load on the drive train. Controllers are designed to adjust the generated torque for maximum power output. Estimation of effective wind speed is required to achieve the above objectives. In this work the estimation of effective wind speed is done by using the Modified Newton Rapshon (MNR), Neural Network (NN) trained by different training algorithms and nonlinear time series based estimation. Initially the control strategies applied was the classical ATF (Aerodynamic torque feed forward) and ISC (Indirect speed control), however due their weak performance and unmodeled WT disturbances, nonlinear static and dynamic feedback linearization techniques with the above wind speed estimators are proposed.     

EVALUATION OF PROBABILITY DENSITY FUNCTIONS IN PRECIPITATION MODELS FOR THE PACIFIC NORTHWEST1

ABSTRACT: Recent work has found that a one-parameter Weibull model of wet day precipitation amount based on the Weibull distribution provides a better fit to historical daily precipitation data for eastern U.S. sites than other one-parameter models. The general two-parameter Weibull distribution was compared in this study to other widely used distributions for describing the distribution of daily precipitation event sizes at 99 sites from the U.S. Pacific Northwest. Surprisingly little performance was sacrificed by reducing the two-parameter Weibull to a single-parameter distribution. Advantages of the single-parameter model included requiring only the mean wet day precipitation amount for calibration, invertibility for simulation purposes, and ease of analytical manipulation. The fit of the single-parameter Weibull to the 99 stations included in this study was significantly better than other single-parameter models tested, and performed as well as the widely endorsed, more cumbersome, two-parameter gamma model. Both the one-and two-parameter Weibull distributions are shown to have b-moments that are consistent with historical precipitation data, while the ratio of b-skew and b-variance in the gamma model is inconsistent with the historical recerd by this measure. In addition, it was found that the two-parameter gamma distribution was better fit using the method of moments estimators than maximum likelihood estimates. These findings suggested that the distribution in precipitation among sites in the Pacific Northwest with dramatically different settings are nearly identical if expressed in proportion to the mean site event size.     

The effects of sinusoidal leading edge of turbine blades on the power coefficient of horizontal-axis wind turbine (HAWT)

In order to improve the aerodynamic performance of horizontal-axis wind turbine (HAWT), a sinusoidal shape is applied to turbine blade. In this study, four types of modified blades were chosen based on variations in amplitude and wavelength of protuberance along the leading edge. Compared with the baseline model, the power coefficients (C_p) of HAWT with modified blades were improved, especially at low tip speed ratios. At low wind speed (V = 6 m/s), blades with short wavelength obtain significant improvement in C_p compared with the baseline model. As wind speed increases, this improvement decreases. In addition, turbine blade with large amplitude and long wavelength obtains better C_p values at higher wind speeds than lower ones, which have a great potential to be more superior at relatively higher wind speeds.     

On the wind resource mapping of Burkina Faso

In this work, mesoscale wind resource maps, at 5-km resolution, of the country of Burkina Faso (274,200 km²) were developed using the Anemoscope and mesoscale compressible community models. Results show that the northeast region of Burkina Faso has a good wind regime at 80 m above ground level (agl), while the wind regime in other parts of the country is generally low, even at 80 m agl. In addition, the technical power potential and the potential annual energy production that can be generated from the wind in Burkina Faso are identified using analysis tools based on geographical information systems and economic constraints. Results from the technical power potential at 80 m agl show that a total of 312 MW of wind farms, generating annually a total of 741 GWh of energy, could be installed in Burkina Faso. On the other hand, a total of 4411 MW of small wind turbines (50 kW) could be installed over the territory, corresponding to an annual energy production of 7843 GWh. The Wind Atlas of Burkina Faso provides an opportunity for local stakeholders to consider wind energy for the electricity portfolio of the country.     

Contrasting the core beliefs regarding the effective implementation of wind power. An international study of stakeholder perspectives

This paper analyses patterns in beliefs about the implementation of wind power as part of a geographical comparison of onshore wind power developments in the Netherlands, North-Rhine Westphalia and England. Q methodology is applied, in order to systematically compare the patterns in stakeholder views on the institutional conditions and changes in the domains of energy policy, spatial planning and environmental policy. Three factors represent support for wind power implementation from fundamentally different perspectives. The fourth perspective is critical opposed to wind power developments as well as critical to the manner in which wind projects are proposed, planned and implemented. These four perspectives exist across the geographical cases; however, some perspectives are prominent in one case and marginal in another. This relates to different legacies and varying implementation achievements in the three cases. The analysis shows that an approach that focuses on implementing as much wind power as possible, relying on technocratic reasoning and hierarchical policies is in practice the least successful, whereas collaborative perspectives with more emphasis on local issues and less on the interests of the conventional energy sector were particularly dominant in the most successful case, North-Rhine Westphalia.     

Availability estimation of a multi-state wind farm in fuzzy environment

Wind is one of the fastest growing renewable energy resources in the electric power system. Availability of wind energy is volatile in nature due to the stochastic behavior of wind speed and non-linear variation of the wind power curve of wind turbine generator. Because of this impression and uncertainty, the availability estimation of wind power has become a challenging issue. In this paper, Markov Fuzzy Reward technique has been proposed for finding out the reliability of wind farm by assessing the availability of wind power. According to this technique, availability of the wind power has been estimated considering wind farm and demand both as a multi-state system. In addition to the availability, different reliability indices such as the number of absolute failures, mean time to deficiency, and probability of failures of a wind farm have been assessed in a time horizon, which can provide useful information for the power system planner at wind farm installing stage. A comparison of this study reveals the efficacy of the proposed Markov Fuzzy Reward approach over the conventional Markov Reward approach.     

Offshore wind power potential of the Black Sea region in Turkey

Majority of the studies on offshore wind power potential assessment is limited to the examination of the wind speed only. This study examines the offshore wind power potential of the Black Sea coastal region in Turkey based on location selection criteria including territorial waters, military areas, civil aviation, shipping routes, pipelines and underground cables, social, and environmental concerns. Wind Atlas Analysis and Application Program (WAsP) is used to do the statistical analysis of wind speed and wind direction data for 20 locations in the Black Sea coastal region. WAsP results are then elaborated based on the location selection criteria for better assessment of offshore wind power utilization. The study reveals that there are limited numbers of locations for offshore wind power generation in the Black Sea region in spite of its long coastline. Moreover, there is a high need for a zoning change for Amasra shores in order to utilize high offshore wind power potential of Amasra. Our finding suggests that location selection criteria other than wind speed should definitely be considered for better assessment of the wind power potential of a region.