Previsional estimation of the energy output of windgenerators

This paper illustrates a simple method for evaluating the energy output of windgenerators of known main characteristics in a site of known wind typology. The method is based on the matching of a model of the WECS with the Weibull model of wind regime. To enter the method the following quantities must be known: Weibull shape parameter and mean wind speed, turbine diameter, hub height, cut-in and nominal wind speeds and nominal power. With these quantities one can enter a diagram that gives the value of the plant utilisation factor for any specific siting. An example shows how to make a choice among several models of small size.     

Modelling of turbulent wind flow using the embedded Markov chain method

Small wind turbines are usually installed to provide off-grid power and as such can be situated close to the load in a less-than-ideal wind resource. These wind regimes are often governed by low mean speeds and high wind turbulence. This can result in energy production less than that specified by the manufacturer's power curve. Wind turbulence is detrimental to the fatigue life of key components and overall turbine reliability and therefore must be considered in the design stage of small wind turbines. Consequently it is important to accurately simulate wind speed data at highly turbulent sites to quantify loading on turbine components. Here we simulate wind speed data using the Markov chain Monte Carlo process and incorporate long term effects using an embedded Markov chain. First, second and third order Markov chain predictions were found to be in good agreement with measured wind data acquired at 1 Hz. The embedded Markov chain was able to predict site turbulent intensity with a reasonable degree of accuracy. The site exhibited distinctive peaks in wind speed possibly caused by diurnal heating and cooling of the earth's surface. The embedded Markov chain method was able to simulate these peaks albeit with a time offset.     

Assessment of wind characteristics and wind turbine characteristics in Taiwan

Wind characteristics and wind turbine characteristics in Taiwan have been thoughtfully analyzed based on a long-term measured data source (1961–1999) of hourly mean wind speed at 25 meteorological stations across Taiwan. A two-stage procedure for estimating wind resource is proposed. The yearly wind speed distribution and wind power density for the entire Taiwan is firstly evaluated to provide annually spatial mean information of wind energy potential. A mathematical formulation using a two-parameter Weibull wind speed distribution is further established to estimate the wind energy generated by an ideal turbine and the monthly actual wind energy generated by a wind turbine operated at cubic relation of power between cut-in and rated wind speed and constant power between rated and cut-out wind speed. Three types of wind turbine characteristics (the availability factor, the capacity factor and the wind turbine efficiency) are emphasized. The monthly wind characteristics and monthly wind turbine characteristics for four meteorological stations with high winds are investigated and compared with each other as well. The results show the general availability of wind energy potential across Taiwan.     

A new semi-empirical wind turbine capacity factor for maximizing annual electricity and hydrogen production

The capacity factor is an important wind turbine parameter which is ratio of average output electrical power to rated electrical power of the wind turbine. Another main factor, the AEP, the annual energy production, can be determined using wind characteristics and wind turbine performance. Lower rated power may lead to higher capacity factor but will reduce the AEP. Therefore, it is important to consider simultaneously both the capacity factor and the AEP in design or selecting a wind turbine. In this work, a new semi-empirical secondary capacity factor is introduced for determining a rated wind speed at which yearly energy and hydrogen production obtain a maximum value. This capacity factor is expressed as ratio of the AEP for wind turbine to yearly wind energy delivered by mean wind speed at the rotor swept area. The methodology is demonstrated using the empirical efficiency curve of Vestas-80 2 MW turbine and the Weibull probability density function. Simultaneous use of the primary and the secondary capacity factors are discussed for maximizing electrical energy and hence hydrogen production for different wind classes and economic feasibility are scrutinized in several wind stations in Kuwait.     

Reliably model of microwind power energy output under real conditions in France suburban area

Micro-wind turbine are now specially designed for rural or urban environment and one of the main advantages of such turbine is that it can be propelled by a wind speed as low as 3 m/s. However, due to terrain roughness in urban environments wind flow is reduced compared to open spaces reducing power output and increasing payback time on capital investment. Well mounting turbines in urban areas may provide the perfect opportunity for onsite generation from wind power. In this paper, we investigate the performance of a micro-wind turbine in a complex urban area and show that due to long time period and very subtile onsite measurements the ideal position for the wind turbine can be determined. Well measured data, wind speed, power output at this particular location are approximated by the Weibull function. The considered model is tested and validated at an urban landscape location in Metz City, France, where an anemometry is positioned at adjacent to the turbine and the instrumentation is positioned specific to its surrounding location and, record wind turbine data thanks to real time wireless communications. Technical data including wind speed and output power were analyzed and reported allowing to provide an reliable estimation of the wind energy potential in an urban location.     

A new probabilistic method to estimate the long-term wind speed characteristics at a potential wind energy conversion site

This paper proposes the use of a new Measure-Correlate-Predict (MCP) method to estimate the long-term wind speed characteristics at a potential wind energy conversion site. The proposed method uses the probability density function of the wind speed at a candidate site conditioned to the wind speed at a reference site. Contingency-type bivariate distributions with specified marginal distributions are used for this purpose. The proposed model was applied in this paper to wind speeds recorded at six weather stations located in the Canary Islands (Spain). The conclusion reached is that the method presented in this paper, in the majority of cases, provides better results than those obtained with other MCP methods used for purposes of comparison. The metrics employed in the analysis were the coefficient of determination (R²) and the root relative squared error (RRSE). The characteristics that were analysed were the capacity of the model to estimate the long-term wind speed probability distribution function, the long-term wind power density probability distribution function and the long-term wind turbine power output probability distribution function at the candidate site.     

Wind turbine power curve modelling using artificial neural network

Technical improvements over the past decade have increased the size and power output capacity of wind power plants. Small increases in power performance are now financially attractive to owners. For this reason, the need for more accurate evaluations of wind turbine power curves is increasing. New investigations are underway with the main objective of improving the precision of power curve modeling. Due to the non-linear relationship between the power output of a turbine and its primary and derived parameters, Artificial Neural Network (ANN) has proven to be well suited for power curve modelling. It has been shown that a multi-stage modelling techniques using multilayer perceptron with two layers of neurons was able to reduce the level of both the absolute and random error in comparison with IEC methods and other newly developed modelling techniques. This newly developed ANN modeling technique also demonstrated its ability to simultaneously handle more than two parameters. Wind turbine power curves with six parameters have been modelled successfully. The choice of the six parameters is crucial and has been selected amongst more than fifty parameters tested in term of variability in differences between observed and predicted power output. Further input parameters could be added as needed.     

Investigation on wind power potential on Hong Kong islands—an analysis of wind power and wind turbine characteristics

This paper discusses the potential for electricity generation on Hong Kong islands through an analysis of the local weather data and typical wind turbine characteristics. An optimum wind speed, u_op, is proposed to choose an optimal type of wind turbine for different weather conditions. A simulation model has been established to describe the characteristics of a particular wind turbine. A case study investigation allows wind speed and wind power density to be obtained using different hub heights, and the annual power generated by the wind turbine to be simulated. The wind turbine's capacity factor, being the ratio of actual annual power generation to the rated annual power generation, is shown to be 0.353, with the capacity factor in October as high as 0.50. The simulation shows the potential for wind power generation on the islands surrounding Hong Kong.     

Wind characterization analysis incorporating genetic algorithm: A case study in Taiwan Strait

In this paper, the genetic algorithm (GA) is originally applied to compute the Weibull parameters for wind characterization analysis, in which an objective function required in GA for searching optimization solution has been first defined as well. Wind data analyzed are observed at a wind farm in the Taiwan Strait from 2006 to 2008. To accurately describe wind speed distribution three kinds of probability density functions are compared, i.e. the Weibull, logistic and lognormal functions. Statistical parameters including the max error in the Kolmogorov-Smirnov test, root mean square error, Chi-square error and relative error of wind power density are considered as judgment criterions. The results show that GA is a useful method, there is about 33% time saving when compared with conventional iteration method. Weibull function describes best the wind distribution, regardless of time periods. Accordingly, wind power density, availability factor and electrical energy output from an ideal turbine are assessed using the Weibull parameters; utilization rate of wind energy for the currently used turbine is discussed. Further the wind energy compensates very well with solar energy; when solar radiation is down in winter and spring, the wind power becomes greater; energy ratios for each month are calculated lastly.     

Normalized power curves as a tool for identification of optimumwind turbine generator parameters

This paper presents a novel method of matching wind turbine generators to a site using normalized power and capacity factor curves. The site matching is based on identifying optimum turbine speed parameters from turbine performance index curve, which is obtained from the normalized curves, so as to yield higher energy production at higher capacity factor. The wind speeds are parameterized using cubic mean cuberoot and statistically modeled using Weibull probability density function. An expression for normalized power and capacity factor, expressed entirely in normalized rated speed, is derived. Wind Turbine Performance Index, a new ranking parameter, is defined to optimally match turbines to a potential wind site. The plots of normalized power, capacity factor and turbine performance index versus normalized rated wind speed are drawn for a known value of Weibull shape parameter of a site. Usefulness of these normalized curves for identifying optimum wind turbine generator parameters for a site is presented by means of two illustrative case studies. The generalized curves, if used at the planning and development stages of wind power stations, will serve as useful tool to make a judicious choice of a wind turbine generator that yields higher energy at higher capacity factor     

A study of the performance benefits of closely-spaced lateral wind farm configurations

Scaled wind turbine experiments were conducted in order to evaluate the beneficial effect of closely-spaced lateral wind turbine configurations on the performance of a wind farm. Two outer wind turbines were spaced apart with a particular gap distance and the longitudinal setback of a central rotor was varied at each gap width. The turbine placement resulted in tip-to-tip separation distances that ranged from 1 diameter (D) to 0.25D. Additionally, the performance of a wind farm layout in rough and smooth boundary layers, designed to mimic onshore and offshore conditions, respectively, was evaluated. It was observed that a narrow gap between several laterally-aligned rotors creates an in-field blockage effect that results in beneficial flow acceleration through the gap. This increase in speed increases the power output of the central turbine when its longitudinal setback is between 0D and 2.5D. A cumulative increase in power output of 17% was observed when 3 rotors were aligned in a lateral plane with a blade tip separation of 0.5D or 0.25D, compared to the same number of rotors in isolation. While the benefits of closely-spaced wind turbines were observed in both of the tested boundary layers, the performance benefits with a smooth boundary layer were smaller than with a rough boundary layer. These results may lead to new wind farm design methodologies for certain topology- and wind distribution-specific sites and suggest that wind turbines can be closely-spaced in the lateral direction in order to obtain substantial increases in power.     

On the energy output estimation of wind turbines

During the operation of the German test field for small Wind Energy Conversion Systems (WECS) on the island of Pellworm five wind turbines were tested following recommendations of the International Energy Agency (IEA) expert group. Possible errors in the estimation of a tested wind turbine's total energy output at a potential installation site are investigated. Different wind speed frequency distributions (the measured one, the Rayleigh and the two-parameter Weibull distribution) are used to calculate the total energy output. The differences between the various distributions are mostly below 10 per cent. An improvement of the energy output estimate by a Weibull-instead of a Rayleigh distribution was not found. It is also shown that the use of the recommended 10 min averages or any other average overestimates the WECS' efficiency, up to 14 per cent on average depending on turbulence intensity. Wind power instead of wind speed is the appropriate parameter for power performance testing. Spectra of wind power and electrical power output show three areas of different correlation. A resistance length for wind turbines is shown to be dependent on the WECS operation status.     

Identification-based prediction of wind park power generation

This paper approaches the problem of output power prediction for an off-shore wind park. To this end, a so called wind deficiency factor for each turbine and for each wind direction sector is identified using past data. This identification is done by using the effective wind speed concept that can establish a link between output power of each wind turbine and meteorological mast measures in terms of wind speed and direction. Based on forecast wind speed and direction, a wind park simulator that uses the previously-identified deficiency factors, computes future output power time evolutions. Numerical simulations show the feasibility of the proposed approach.     

An integrated control method for a wind farm to reduce frequency deviations in a small power system

Output power of wind turbine generator (WTG) is not constant and fluctuates due to wind speed changes. To reduce the adverse effects of the power system introducing WTGs, there are several published reports on output power control of WTGs detailing various researches based on pitch angle control, variable speed wind turbines, energy storage systems, and so on. In this context, this paper presents an integrated control method for a WF to reduce frequency deviations in a small power system. In this study, the WF achieves the frequency control with two control schemes: load estimation and short-term ahead wind speed prediction. For load estimation in the small power system, a minimal-order observer is used as disturbance observer. The estimated load is utilized to determine the output power command of the WF. To regulate the output power command of the WF according to wind speed changing, short-term ahead wind speed is predicted by using least-squares method. The predicted wind speed adjusts the output power command of the WF as a multiplying factor with fuzzy reasoning. By means of the proposed method, the WF can operate according to the wind and load conditions. In the WF system, each output power of the WTGs is controlled by regulating each pitch angle. For increasing acquisition power of the WF, a dispatch control method also is proposed. In the pitch angle control system of each WTG, generalized predictive control (GPC) is applied to enhance the control performance. Effectiveness of the proposed method is verified by the numerical simulations.     

Simulation of hourly wind speed and array wind power

Statistical summaries of wind speed are sufficient to compute many characteristics of turbine-generated power, such as the mean, variance and reliability of various power levels. However, a wind speed time series is necessary to produce a sequence of power values as used for investigating load matching and storage requirements. Since a long historical record of wind speed may not be available at a wind turbine candidate site, it is desirable to be able to generate a simulated numerical sequence of hourly wind speed values. Two such approximate procedures are developed in this paper. One procedure generates sequential wind speed values at a site based on the Weibull parameters of hourly wind speed and the lag-one autocorrelation of hourly wind speed values. Comparison with historical data at a site is made. The second procedure generates sequential hourly wind power values for a regional array of wind turbines. It utilizes the typical site wind characteristics, the spatial and lag-one cross correlation and autocorrelation of hourly wind speed values and an equivalent linearized relationship between array average wind speed and array power. Comparison with results for six different wind turbines in three different regional arrays indicates good agreement for wind power histograms, autocorrelation function and mean persistence.     

Global sensitivity analysis of wind turbine power output

The dynamics of wind turbine behavior are complex and a critical area of study for the wind industry. Identification of factors that cause changes in turbine performance can sometimes prove to be challenging, whereas other times, it can be intuitive. The quantification of the effect that these factors have is valuable for making improvements to both power performance and turbine health. In commercial farms, large quantities of meteorological and performance data are commonly collected to monitor daily operations. These data can also be used to analyze the relationship between each parameter in order to better understand the interactions that occur and the information contained within these signals. In this global sensitivity analysis, a neural network is used to model select wind turbine supervisory control and data acquisition system parameters for an array of turbines from a commercial wind farm that exhibit signs of wake interaction. An extended Fourier amplitude sensitivity test is then performed for 2 years of 10‐min averaged data. The study examines the primary and combined sensitivities of power output to each selected parameter for two turbines in the array. The primary sensitivities correspond to single parameter interactions, whereas combined sensitivities account for interactions between multiple parameters simultaneously. Highly influential parameters such as wind speed and rotor rotation frequency produce expected results; the extended Fourier amplitude sensitivity test method proved effective at quantifying the sensitivity of a wide range of more subtle inputs. These include blade pitch, yaw position, main bearing and ambient temperatures as well as wind speed and yaw position standard deviation. The technique holds promise for application in full‐scale wake studies where it might be used to determine the benefits of emerging power optimization strategies such as active wake management. The field of structural health monitoring can also benefit from this method. Copyright © 2013 John Wiley & Sons, Ltd.     

Wind resource assessment of an area using short term data correlated to a long term data set

A method of estimating the annual wind energy potential of a selected site using short term measurements related to one year’s recorded wind data at another reference site is presented. The proposed method utilizes the 1-year measured wind speed of one site to extrapolate the annual wind speed at a new site, using an artificial neural network (ANN). In this study, concurrent measurements from target and reference sites over periods of 1-month and 2-month were used to “train” the ANN. Topographical details or other meteorological data are not required for this approach. After derivation of the simulated wind speed time series for the target site, its mean value and its corresponding Weibull distribution parameters are calculated. The derived Weibull distribution of the simulated wind speed is used to make an assessment of the annual wind energy resource in the new area with respect to a particular wind turbine model. Three pairs of measuring stations in the southwest of Ireland were examined, where the wind potential is high and technically exploitable. Analysis of the measurements showed a reasonable cross-correlation coefficient of the wind speed between the sites. Results indicate that with this method, only a short time period of wind data acquisition in a new area might provide the information required for a satisfactory assessment of the annual wind energy resource. To evaluate the accuracy of the method, simulation results of the 1-month and 2-month training periods are compared to the corresponding actual values recorded at the sites. Also, a comparison with the results of a commercial wind energy assessment software package is presented showing similar results.     

Wind power potential in Turkey and Akhisar case study

Wind energy potential in various parts of Turkey is becoming economical due to reductions in the wind turbine costs, and in fossil fuel atmospheric pollution. The global change program imposes restrictions for use of alternative renewable and environmentally friendly energy sources. Wind energy is among such energy potentials and its practical and economical use gain significance day by day. The first wind energy turbine site investigation and wind power application possibility have been presented for the Akhisar area within the eastern provinces of Turkey. Different wind turbine technologies are assessed according to the local wind speed variations. Locally and technologically suitable wind turbines are selected. Finally their locations are decided by expert views and field measurements with the usage of well known WASP software. It is calculated that a minimum of 31436 MWh/year wind can be generated in this site. In the calculations 10% error possibility is allowed.     

加装后缘小翼的垂直轴风力机输出特性研究

为了研究H型垂直轴风力机后缘加装小翼的输出特性变化规律,文章以NACA0012翼型叶片为例,采用风洞试验与数值模拟的方法,对加装后缘小翼的风力机进行了研究。模拟结果表明,加装后缘小翼的风力机的单叶片扭矩系数及功率性能要优于未加装小翼的风力机,整体功率较未加装小翼的风力机略有提升。风洞实验结果表明:加装后缘小翼可以提高风力机的最大输出功率,其中径长比对于加装小翼的垂直轴风力机功率提升的影响较大;当转速小于300 r/min时,安装径长比为0.6的后缘小翼的风力机输出功率最高;当转速超过300 r/min时,径长比为0.4的后缘小翼的风力机输出功率最高。     

Characteristics of a highly efficient propeller type small wind turbine with a diffuser

We studied the improved effects a diffuser had on the output power of small wind turbine systems, aiming to introduce these systems to radio relay stations as an independent power supply system. A frustum-shaped diffuser was chosen from an economical standpoint and wind speed distribution. The effect the diffuser's shape had on the wind speed was analyzed by simulation and showed that the wind speed in the diffuser was greatly influenced by the length and expansion angle of the diffuser, and maximum wind speed increased 1.7 times with the selection of the appropriate diffuser shape. The wind speed in the diffuser was fastest near the diffuser's entrance. We conducted field tests using a real examination device with a diffuser and confirmed that the output power of the wind power generator increased by up to 2.4 times compared to that of a conventional turbine. Moreover, it was confirmed that the diffuser was especially useful where the wind direction was constant.