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.     

Wind power potential assessment for three buoys data collection stations in the Ionian Sea using Weibull distribution function

The present article utilizes wind measurements from three buoys data collection stations in Ionian Sea to study the wind speed and power characteristics using the Weibull shape and scale parameters. Specifically, the site dependent, annual, and monthly mean patterns of mean wind speed, Weibull parameters, frequency distribution, most probable wind speed, maximum energy carrying wind speed, wind power density and wind energy density characteristics have been analyzed. The Weibull distribution was found to represent the wind speed distribution with more than 90% accuracy, in most of the cases. Moreover, the correlation between the percentages of times the wind speed was above cut-in-speed and the measured mean wind speed for the three selected sites, as the correlation between the aforementioned percentages and the scale parameter c were examined and were found linear. At all these sites, no definite increasing or decreasing trends in annual mean wind speed values could be detective over the data reporting period. The mean values of wind speed, scale parameter, most probable wind speed, maximum energy carrying wind speed, wind power and wind energy density values showed higher values during winter time and lower in summer time in Pylos and Zakynthos. Moreover, Pylos and Zakynthos were found to be the best sites from wind power harnessing point of view.     

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.     

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.     

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.     

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.     

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。     

一个研究街道峡谷流场及浓度场特征的三维数值模式

目前，研究街道峡谷内流场及机动车排放污染物的扩散行为特征所采用的主要方法为：采用野外测试法和物理模拟法，而采用三维数值模拟方法研究此问题的工作很少。本文创建了一个研究微尺度街道峡谷内流场及机动车排放污染物扩散特征的三维数值模式，即首次采用伪不定常方法，利用K——E闭合方案，建立了一个模拟城市街道峡谷内流场及污染物扩散特征与街道峡谷风场、街道几何结构及两侧建筑物高度对称性之间的复杂关系的三维数值模式。经过与实际监测资料及风洞实验对比，结果表明此三维模式具有较好的模拟精度，能够很好模拟峡谷内的风场及街谷几何结构对街道峡谷内流场及浓度场特征的影响，有很强的实用性。     

LAKE SHORE PROTECTION AGAINST WIND-GENERATED WAVES1

ABSTRACT: A method is reported for estimating the height of wind waves in any lake for a given wind condition. Maximum wind speeds from five climatological stations in and around Ilinois for the period of 1950–1972 were analyzed and the maximum wind speed for various durations and return periods were presented. Statistical analysis of wind wave data collected from Carlyle Lake indicated the Rayleigh distribution fitted the wave height distribution reasonably well and that the nondimensional energy spectra followed the (f/f_m)^-5 rule in the equilibrium range of frequencies. From a consideration of various forces and physical properties of riprap particles and water, a relationship was developed to estimate the stable weight of riprap particles. A practical design criteria is proposed to stabilize lake shores against wind waves.     

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.     

Best location of the small wind turbine around a single high-rise building

This research is a three-dimensional investigation about the aerodynamic interaction between the wind flow and a single high-rise building. In order to find location(s) with high potential of velocity around the building, a wide variety of wind speeds ranging from 2 to 10 m/s is studied. On the other hand, a high-rise building with the ratio of height to width of H/W = 3 is considered. Computations are performed numerically by means of the finite volume approach. Several results are obtained in the present numerical study. For example, it is found that due to wind-structure vertical interaction, locations with enhanced velocities are developed on the building roof in which the rate of this enhancement increases with increasing the wind speed. In addition, over the building, “lines C and D” are realized as the best locations having high power potentials and low turbulence intensities. In addition, lateral wind-structure interaction revealed that for all wind speeds, location of L/W = 0.5 is the best for the small wind turbine installation.     

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.     

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 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 comparative analysis of wind resource parameters using WAsP and windPRO

This paper investigates the accuracy of the wind resource estimation for a site in a central India region using a latest licensed version of WAsP 11 and windPRO 3.1. Whole one year measured met mast wind data has been taken using anemometer and wind vane at 10 m and 25 m height, respectively above ground level. The digitized elevation and roughness model of the corresponding site shows the roughness class 4 (roughness length 1.2525 m). The wind data has been extrapolated up to 80 m height by using power and log law models which provide the power density near about 120 W/m². As per the micro sitting guidelines for the virtual wind farm installation 5D X 7D mapping has been selected which Indicates the total power output by installing 8 Vestas V-90 1.8 MW wind turbine from WAsP is 31.561 GWh and from windPRO is 28.083 GWh.     

Blade thickness effect on the aerodynamic performance of an asymmetric NACA six series blade vertical axis wind turbine in low wind speed

ABSTRACT

Vertical axis wind turbine (VAWT) is an economic and widely used energy converter for converting wind energy into useful form of energy, like mechanical and electrical energy. For efficient energy conversion in low wind speed and to have improved power coefficient of asymmetric blade VAWT, selection of optimum blade thickness is needed thus entailing its detailed investigation with respect to different operating wind speed conditions. Present study methodically explores the impact of thickness to chord (t/c) ratio on aerodynamic performance of a three bladed asymmetrical blade H-Darrieus VAWT at different low wind speed conditions by using 2D unsteady CFD simulations. The optimal t/c is obtained on the basis of maximum power coefficient and average moment coefficient of the turbine. The aerodynamic performance curves are obtained at different operating and t/c conditions and the performance insights are corroborated with the findings from the flow physics study to come to some concrete conclusions on the effects of the thickness to chord ratio. The present study identifies large blade curvature to create a large diverging passage on the blade suction surface as the prominent reason for aerodynamic performance drop at a high t/c ratio.     

A novel wind speed forecasting model for wind farms of Northwest China

Wind resources are becoming increasingly significant due to their clean and renewable characteristics, and the integration of wind power into existing electricity systems is imminent. To maintain a stable power supply system that takes into account the stochastic nature of wind speed, accurate wind speed forecasting is pivotal. However, no single model can be applied to all cases. Recent studies show that wind speed forecasting errors are approximately 25% to 40% in Chinese wind farms. Presently, hybrid wind speed forecasting models are widely used and have been verified to perform better than conventional single forecasting models, not only in short-term wind speed forecasting but also in long-term forecasting. In this paper, a hybrid forecasting model is developed, the Similar Coefficient Sum (SCS) and Hermite Interpolation are exploited to process the original wind speed data, and the SVM model whose parameters are tuned by an artificial intelligence model is built to make forecast. The results of case studies show that the MAPE value of the hybrid model varies from 22.96% to 28.87 %, and the MAE value varies from 0.47 m/s to 1.30 m/s. Generally, Sign test, Wilcoxon’s Signed-Rank test, and Morgan--Granger--Newbold test tell us that the proposed model is different from the compared models.     

Short-term wind speed forecasting based on random forest model combining ensemble empirical mode decomposition and improved harmony search algorithm

ABSTRACT

Wind speed forecasting plays an important role in power grid dispatching management. This article proposes a short-term wind speed forecasting method based on random forest model combining ensemble empirical modal decomposition and improved harmony search algorithm. First, the initial wind speed data set is decomposed into several ensemble empirical mode functions by EEMD, then feature extraction of each sub-modal IMF is performed using fast Fourier transform to solve the cycle of each sub-modal IMF. Next, combining the high-performance parameter optimization ability of the improved harmony search algorithm, two optimal parameters of random forest model, number of decision trees, and number of split features are determined. Finally, the random forest model is used to forecast the processing results of each submodal IMF. The proposed model is applied to the simulation analysis of historical wind data of Chaoyang District, Liaoning Province from April 27, 2015 to May 22, 2015. To illustrate the suitability and superiority of the EEMD-RF-IHS model, three types of models are used for comparison: single models including ANN, SVM, RF; EMD combination models including EMD-ANN, EMD-SVM, EMD-RF; EEMD combination models including EEMD-ANN, EEMD-SVM, EEMD-RF. The analysis results of evaluation indicators show that the proposed model can effectively forecast short-term wind data with high stability and precision, providing a reference for forecasting application in other industry fields.     

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.     

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.