Evaluating the wind speed persistence for several wind stations in Peninsular Malaysia

An important factor to consider when evaluating wind energy potential is the wind speed persistence. In this study, persistence of the wind speed in Peninsular Malaysia is investigated based on the hourly data available at 10 wind stations from 2007 to 2009. To determine the degree of persistence in the data for each station, stationarity and variability are investigated using unit-root tests and the test for equality of variance respectively. Results from the unit-root tests indicated that the hourly wind speed for each station exhibits stationarity. The test for equality of variance, based on Levene’s test, shows that there exists a significant difference in the variability of wind speed between the different stations. Because the variance of the hourly wind speeds for the Chuping station is the smallest observed, the wind speed observed at this location is the most persistent compared to other locations. However, it is more meaningful to measure the persistence at a particular level of speed, one suitable to generate energy. Accordingly, the wind speed duration curve method is applied to the observed data for each station. Consequently, the wind speed at Mersing is found to be the most persistent, and, consequently, this location has the most potential for energy production compared to other locations.     

Application of artificial neural networks for the wind speed prediction of target station using reference stations data

In this study, artificial neural networks (ANNs) were applied to predict the mean monthly wind speed of any target station using the mean monthly wind speeds of neighboring stations which are indicated as reference stations. Hourly wind speed data, collected by the Turkish State Meteorological Service (TSMS) at 8 measuring stations located in the eastern Mediterranean region of Turkey were used. The long-term wind data, containing hourly wind speeds, directions and related information, cover the period between 1992 and 2001. These data were divided into two sections. According to the correlation coefficients, reference and target stations were defined. The mean monthly wind speeds of reference stations were used and also corresponding months were specified in the input layer of the network. On the other hand, the mean monthly wind speed of the target station was utilized in the output layer of the network. Resilient propagation (RP) learning algorithm was applied in the present simulation. The hidden layers and output layer of the network consist of logistic sigmoid transfer function (logsig) and linear transfer function (purelin) as an activation function. Finally, the values determined by ANN model were compared with the actual data. The maximum mean absolute percentage error was found to be 14.13% for Antakya meteorological station and the best result was found to be 4.49% for Mersin meteorological station.     

Spatial estimation of wind speed

This study utilizes Abductory Induction Mechanism to estimate the mean monthly wind speed at some locations in Saudi Arabia based on wind data at other available recording stations in addition to some historical wind speed data at the target site. Wind speed data from 20 meteorological stations over a period of 16 years between 1990 and 2005 was used to accomplish the set objective. To validate the model, data from 19 stations was used to estimate the wind speed at the 20th location. Evaluation was performed for every one of the 20 available locations. Results show good agreement between estimated and measured monthly mean wind speed values. Copyright © 2010 John Wiley & Sons, Ltd.     

Characterization of wind speed data according to wind direction

Knowledge of the wind speed distribution and the most frequent wind directions is important when choosing wind turbines and when locating them. For this reason wind evaluation and characterization are important when forecasting output power. The data used here were collected from eleven meteorological stations distributed in Navarre, Spain. We obtained data for the period extending from 1992 to 1995, with each datum encompassing 10 minutes of time. Wind speed data of each station were gathered in eight directional sectors, each one extended over 45 degrees according to the direction from which the wind blows. The stations were grouped in two blocks: those under the influence of the Ebro valley and those in mountainous areas. For each group the Weibull parameters were estimated, (according to the Weibull probability paper because the Weibull distribution gives the best fit in this region). Kurtosis and skewness coefficients were estimated as well. The Weibull parameters, especially the scale parameter c, depend strongly on the direction considered, and both Weibull parameters show an increasing trend as the direction considered moves to the more dominant direction, while both kurtosis and skewness show a corresponding decreasing trend.     

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.     

Variogram time-series analysis of wind speed

Fluctuations of wind-power production are a significant hindrance to its high penetration in power systems. System operators have to provide complementary power and relevant control strategies to smooth out the fluctuations when large-scale wind power ones is injected into the grid. To better smooth the fluctuations, the change rate of the wind speed is a critical piece of information. In this study, the variogram function is introduced to measure the change rate of the wind speed. Based on the variogram time-series, some statistical analyses are conducted. These results contribute to a better understanding of the characteristics of the change rate of the wind speed, such as the chronological variation pattern of the change rate on a day, whether the future change rate can be forecasted, and whether there is a relationship between the change rate and wind speed.     

低风速离网型风力发电机的叶片设计

根据内蒙古四子王旗某地2007年逐月平均风速表选取设计风速为6 m/s,根据某型号300 W交流永磁发电机的输出功率特性,利用Matlab编写的叶片优化设计程序,设计了300 W水平轴风力发电机叶片.通过风轮与发电机的匹配,调整叶片参数,使之在小风时具有较高的风能利用系数,提高风力机的效率,在大风时具有较低的风能利用系数,从而达到大风时限速保护的目的.     

全程变桨距风能转换装置的研究

为了实现风力机的最大能量转换，提出了在整个工作风速范围内采用风轮正面的风压作为变桨距的控制信号，实现最大功率跟踪变桨距。通过计算，设计制造出了风压式全程变桨距风能转换装置。试验结果显示：该风能转换装置的高效运行范围从一个极小的区间扩展为一个较宽的区间；当风速大于额定风速时，风力机仍然能够保持稳定的工作转速，在起动前使桨叶处于阻风状态，起动力矩增大了近20倍。     

Dynamic models of wind farms with fixed speed wind turbines

The increasing wind power penetration on power systems requires the development of adequate wind farms models for representing the dynamic behaviour of wind farms on power systems. The behaviour of a wind farm can be represented by a detailed model including the modelling of all wind turbines and the wind farm electrical network. But this detailed model presents a high order model if a wind farm with high number of wind turbines is modelled and therefore the simulation time is long. The development of equivalent wind farm models enables the model order and the computation time to be reduced when the impact of wind farms on power systems is studied. In this paper, equivalent models of wind farms with fixed speed wind turbines are proposed by aggregating wind turbines into an equivalent wind turbine that operates on an equivalent wind farm electrical network. Two equivalent wind turbines have been developed: one for aggregated wind turbines with similar winds, and another for aggregated wind turbines under any incoming wind, even with different incoming winds.The proposed equivalent models provide high accuracy for representing the dynamic response of wind farm on power system simulations with an important reduction of model order and simulation time compare to that of the complete wind farm modelled by the detailed model.     

High value wind: A method to explore the relationship between wind speed and electricity locational marginal price

Wind and solar resources are, by nature, spatially distributed and temporally variable. The process of siting generators that use these renewable resources and integrating them into the electricity system therefore raises different issues than the same process for combustion facilities does. A method for discovering wind power sites with the highest value to the electricity system was developed and is illustrated here using data for the state of Michigan. This method combines readily available hourly average 10 m wind speed data with wholesale electricity price data, as hourly locational marginal price (LMP). The 10 m wind speed data from 72 sites were extrapolated vertically to 80 m turbine hub height, converted to wind power density, and interpolated horizontally via kriging to reconstruct a continuous surface. LMP data from 178 generator nodes were allocated across space using Thiessen polygons. High LMP was interpreted as a signal of insufficiency or weakness in the electricity system, and wind energy was considered a possible remedy. The method, implemented in a GIS, identifies when and where peaks in LMP and wind power density co-occur and highlights these events as high value. As the drive to incorporate more renewable generators into the electricity system increases, this method will help locate the most desirable sites based on wind resource characteristics and the structure of the larger electricity system. Proposing a new way to think about the value of the wind resource to the electricity system is a primary contribution of this work.     

Convertible wind energy based on predicted wind speed at hub-height

Support vector machine is proposed to find wind speed at higher heights using measurements at lower heights. The mean absolute percentage error between measured and the estimated wind speed at height 40 m is found to be satisfactory. After validation at 40 m, the model was used to calculate the wind speed at hub heights up to 100 m. Annual energy yield was found to be increasing with hub height and, hence, accurate estimation of wind speed at heights becomes essential for realistic wind energy assessment. Furthermore, the plant capacity factor was found to be increasing approximately 1% for each 10-m increase in hub height.     

风力提水机低风速启动解决方案的研究

在分析传统风力提水机低风速启动方案的基础上,提出了变行程机构加配重的方案.设计了交行程机构,完成了配重的计算,并利用Pro/Engineer软件建立虚拟样机,通过仿真结果,得到最佳的配重.     

考虑风速相关性的风电接入能力分析

同一地区的风电场一般建立在相互接近的地理位置上,因此其风速往往呈现出一定的相关性。采用Copula函数建立多风电场的风速相关性模型,继而生成具有相关性的风速分布样本空间。考虑风速的随机性与相关性,应用机会约束规划理论,在满足系统安全可靠运行的前提下,以系统可接入的风电机组装机容量最大化作为优化目标,建立了计算风电穿透功率极限的概率最优潮流模型,并采用一种基于随机模拟技术的粒子群优化算法进行求解。以IEEE30节点测试系统为算例,分析风速相关性、风电场接入点和机会约束置信水平对风电接入能力的影响,结果验证了所提模型与算法的合理性与可行性。     

Hourly wind speed analysis in Sicily

The hourly average wind speed data recorded by CNMCA (Centro Nazionale di Meteorologia e Climatologia Aeronautica) have been used to study the statistical properties of the wind speed at nine locations on Sicily. By grouping the observations month by month, we show that the hourly average wind speed, with calms omitted, is represented by a Weibull function. The suitability of the distribution is judged by the discrepancies between the observed and calculated values of the monthly average wind speed and of the standard deviation.     

风电场短期风速预测方法研究

随着风力发电的快速发展,并且风力发电系统的出力与风电场风速存在着的特殊关系,使得对风电场的风速实现较准确的预测已逐步成为研究的热点。该文先提出一种简单的的风速预测方法,即将指数平滑法应用到风速预测,并验证了指数平滑法预测风速的可行性。此外,为了提高预测精度,还提出了两种新的组合预测的方法,即基于指数平滑和灰色模型(GM)的组合预测方法、基于自回归滑动平均(ARMA)模型和灰色模型的组合预测方法。实例计算结果表明,组合预测方法比单独的用一种方法的预测效果要好,尤其是基于自回归滑动平均模型和灰色模型的组合预测方法更具有优势。     

Practical ways of evaluating wind speed persistence

Wind speed persistence is a measure of the mean wind speed duration over a given period of time at any location. This definition implies that wind speed persistence means a positive serial correlation in time series. The wind speed persistence provides useful information about the general climatological characteristics of the wind persisting at a given location. Therefore, wind speed persistence should be taken into account in many studies such as weather forecast, site selection for wind turbines and synthetic generation of the wind speed data. On the other hand, if wind direction information is considered together with the wind speed then this type of persistence can be used for additional purposes such as forest fires, dispersion of the air pollutants, building ventilation, etc. In this study, three different methods with some modifications of the previous methods have been applied to the wind speed data obtained from the meteorology stations located at the northwest part of Turkey. These methods are based on autocorrelation function, conditional probability and the wind speed duration curves. It has been shown that the proposed methods clearly reflect the persistence properties of the wind speed in the study area.     

The influence of the wind speed profile on wind turbine performance measurements

To identify the influence of wind shear and turbulence on wind turbine performance, flat terrain wind profiles are analysed up to a height of 160 m. The profiles' shapes are found to extend from no shear to high wind shear, and on many occasions, local maxima within the profiles are also observed. Assuming a certain turbine hub height, the profiles with hub‐height wind speeds between 6 m s^?1 and 8 m s^?1 are normalized at 7 m s^?1 and grouped to a number of mean shear profiles. The energy in the profiles varies considerably for the same hub‐height wind speed. These profiles are then used as input to a Blade Element Momentum model that simulates the Siemens 3.6 MW wind turbine. The analysis is carried out as time series simulations where the electrical power is the primary characterization parameter. The results of the simulations indicate that wind speed measurements at different heights over the swept rotor area would allow the determination of the electrical power as a function of an ‘equivalent wind speed’ where wind shear and turbulence intensity are taken into account. Electrical power is found to correlate significantly better to the equivalent wind speed than to the single point hub‐height wind speed. Copyright © 2008 John Wiley & Sons, Ltd.     

A hybrid statistical method to predict wind speed and wind power

Accurate forecasting of wind speed and wind power is important for the safety of renewable energy utilization. Compared with physical methods, statistical methods are usually simpler and more suitable for small farms. Based on the methods of wavelet and classical time series analysis, a new short-term forecasting method is proposed. Simulation upon actual time data shows that: (1) the mean relative error in multi-step forecasting based on the proposed method is small, which is better than classical time series method and BP network method; (2) the proposed method is robust in dealing with jumping data; and (3) the proposed method is applicable to both wind speed and wind power forecasting.     

Adequacy assessment of generating systems containing wind power considering wind speed correlation

Wind power is an important renewable energy resource. Electrical power generation from wind energy behaves quite differently from that of conventional sources, and maintaining a reliable power supply is an important issue in power systems containing wind energy. In these systems, the wind speeds at different wind sites are correlated to some degree if the distances between the sites are not very large. Genetic algorithm methods are applied here to adjust autoregressive moving-average time series models in order to simulate correlated hourly wind speeds with specified wind speed cross-correlation coefficients of two wind sites. Multi-state wind energy conversion system models are used to incorporate the correlated wind farms in reliability studies of generating systems. A method to generate random numbers with specified correlation coefficients for application in a state-sampling Monte Carlo simulation technique is introduced. It is shown that the proposed method can be used in the adequacy assessment of a generating system incorporating partially dependent wind farms.