Reliability Evaluation of a Wind Power Delivery System Using an Approximate Wind Model

Large scale wind power penetration has a significant impact on the reliability of the electric generation systems. A wind farm consists of a large number of wind turbine generators (WTGs). A major difficulty in modeling wind farms is that the WTG not have an independent capacity distribution due to the dependence of the individual turbine output on the same energy source, the wind. In this paper, a model of the wind farm output power considering multi-wake effects is established according to the probability distribution of the wind speed and the characteristic of the wind generator output power: based on the simple Jenson wake effect model, the wake effect with wind speed sheer model and the detail wake effect model with the detail shade areas of the upstream wind turbines are discussed respectively. Compared to the individual wake effect model, this model takes the wind farm as a whole and considers the multi-wakes effect on the same unit. As a result the loss of the velocity inside the wind farm is considered more exactly. Furthermore, considering the features of sequentially and self-correlation of wind speed, an auto-regressive and moving average (ARMA) model for wind speed is built up. Also the reliability model of wind farm is built when the output characteristics of wind power generation units, correlation of wind speeds among different wind farms, outage model of wind power generation units, wake effect of wind farm and air temperature are considered. Simulation results validate the effectiveness of the proposed models. These models can be used to research the reliability of power grid containing wind farms, wind farm capacity credit as well as the interconnection among wind farms

Multiple wind farms (WFs) model consists of wind speed model and single WF model. In the multiple WFs integrated power system, the wind speeds at different WFs are correlated to some degree if the distance between WFs is not very large. Hence, a novel MC-Nataf-CD method combining partitions Monte Carlo (MC) sampling, Nataf transformation and Cholesky Decomposition (CD) is proposed to simulate correlated wind speeds with desired cross-correlation coefficients. The wind speeds generated by the proposed wind speed model is closer to both the historical data and the Weibull fitting curve. For the single WF model, wake effects, the forced outage rates (FORs) of wind turbine generators (WTGs) and the reliability of the connected grids inside a WF need to be taken into consideration. Four different single WF models are proposed to reveal the impact of these relevant factors. A statistical method based on state-sampling MC simulation is implemented for the reliability estimation of Roy Billinton Test System (RBTS) with multiple WFs, using both historical and simulated wind speed data. The simulation results and the calculated reliability indices demonstrate that the proposed techniques are effective and accurate in the modeling of correlated multiple WFs.

During recent years, wind energy has moved from an emerging technology to a nearly competitive technology. This fact, coupled with an increasing global focus on environmental concern and a political desire of a certain level of diversification in the energy supply, ensures wind energy an important role in the future electricity market. For this challenge to be met in a cost-efficient way, a substantial part of new wind turbine installations is foreseen to be erected in big onshore or offshore wind farms. This fact makes the production, loading and reliability of turbines operating under such conditions of particular interest.

We report an analysis of trends in 850 hPa wind speed, as manifest in the NCEP–NCAR reanalysis fields, over the Baltic region during the latter half of the 20th century. The results indicate that annual mean wind speeds over the Baltic significantly increased over the period 1953–99 with the majority of the increase being associated with increases in the upper quartile of the wind speed distribution. Accordingly, much of the change is focused on the winter season. The trends in annual and seasonal mean wind speeds are greatest in relative and absolute sense in the southwest of the Baltic basin, where they are in excess of 0.25 m s−1 per decade for the annual mean. The extremes of the wind‐speed distribution also increased by up to 5 m s−1 over the study period for the wind speed with a 50 year return period, again with the largest magnitude changes in the southwestern Baltic. These changes in wind speed are strongly linked to changes in the synoptic‐scale circulation. The majority of the increase in wintertime wind speeds is attributable to an increase in westerly anticyclonic, westerly cyclonic and northwesterly cyclonic circulation types as manifest in the Grosswetterlagen catalogue, which are in turn related to the recent prevalence of the positive phase of the North Atlantic oscillation. Copyright © 2003 Royal Meteorological Society

Cluster-based wind turbine maintenance prioritization for a utility-scale wind farm

A wind resource assessment was conducted for Phases I and II wind farms in Santa Vitoria do Palmar, Brazil. The data used for wind analysis was taken from April 2007 to March 2008 at a meteorological mast height of 101 m. A 10-minute data set of wind speed, standard deviation, wind direction, relative humidity, environmental temperature, and pressure were measured. Analytical estimation models were used to determine the Weibull parameters required for calculating the annual average wind speed, mean power density, cumulative distribution, and probability density functions of the wind regime. The probability density function statistical results were analyzed at two different heights – the reference height of 101 m and the hub height of 80 m. The outcomes were then compared with the WAsP numerical output. The analysis showed that the energy pattern factor method has a confidence level above 97.5% with a correlation coefficient of 100%. Calculation results showed that the annual mean wind speed was 8.10 m/s at 80 m hub height, recommending the site is appropriate for GL Wind 2010 and IEC 61400-1 Ed.2 Class II wind turbine generators. The characteristic wind turbulence at 101 m reference height was IEC Subclass B. Out of the selected wind turbine generators, the HAWT-5/2.50 MW has the highest capacity factor of 44.17% but the lowest levelized energy cost of US$ 44.36/MWh. WAsP was also used to numerically calculate the given locality’s net annual energy production. Based on the proposed wind turbine arrangement, the predicted net annual energy production with wake loss effect for Phases I and II wind farms are 104.92 GWh/year and 456.89 GWh/year, respectively.

Differences in wind farm energy production based on the atmospheric stability dissipation rate: Case study of a 30 MW onshore wind farm

Researching on interaction between wind farm and power system, it usually uses simple model to describe the wind farm. The wind turbines in a wind farm always suppose as a coherent generator group, every wind turbines have identical wind speed. As wind turbine locates in the different site in the wind farm, the wind speed at each wind turbines will not change simultaneously. Wake effect is another factor that should take into consideration. So, it needs to get wind speed equivalent model. The equivalent model of wind speed in the wind farm is based on output of power. Gust wind and ramp wind are discussed. Wind farm will change gust wind into step varying wind and make ramp wind into different type wind. The arrangement of wind turbines will also affect equivalent model of wind speed. The result show that the length of wind farm will smooth the power output. It explains why the power output fluctuation of wind farm is smaller than single wind turbine. The onshore wind farm located in Dongtai Jiangsu province is proposed as an example. The equivalent wind speed model and equivalent impedance connected to grid are calculated. The results illustrate the impact of wind speed model on wind farm output.

Today, most electrical energy is generated by burning huge fossil fuels and special weather conditions such as acid rain and snow, climate change, urban smog, regional haze, several tornados, etc., have happened around the whole world. It is now clear that the installation of a number of wind turbine generators can effectively reduce environmental pollution, fossil fuel consumption, and the costs of overall electricity generation. Although wind is only an intermittent source of energy, it represents a reliable energy resource from a long-term energy policy viewpoint. Among various renewable energy resources, wind power energy is one of the most popular and promising energy resources in the whole world today. At a specific wind farm, the available electricity generated by a wind power generation system depends on mean wind speed (MWS), standard deviation of wind speed, and the location of installation. Since year-to-year variation on annual MWS is hard to predict, wind speed variations during a year can be well characterized in terms of a probability distribution function (pdf). This paper also addresses the relations among MWS, its standard deviation, and two important parameters of Weibull distribution. The wind resource varies with of the day and the season of the year and even some extent from year to year. Wind energy has inherent variances and hence it has been expressed by distribution functions. In this paper, we present some methods for estimating Weibull parameters, namely, shape parameter ( k ) and scale parameter ( c ). The Weibul distribution is an important distribution especially for reliability and maintainability analysis. The suitable values for both shape parameter and scale parameters of Weibull distribution are important for selecting locations of installing wind turbine generators. The scale parameter of Weibull distribution also important to determine whether a wind farm is good or not. The presented method is the analytical methods and computational experiments on the presented methods are reported.

This paper proposes a grouped, reactive power optimization control strategy to maximize the active power output of a doubly-fed induction generator (DFIG) based on a large wind farm (WF). Optimization problems are formulated based on established grouped loss models and the reactive power limits of the wind turbines (WTs). The WTs in the WF are grouped to relieve computational burden. The particle swarm optimization (PSO) algorithm is applied to optimize the distribution of reactive power among groups, and a proportional control strategy is used to distribute the reactive power requirements in each group. Furthermore, the proposed control strategy optimizes the reactive power distribution between the stator and the grid side converter (GSC) in each WT. The proposed control strategy greatly reduces the number of variables for optimization, and increases the calculation speed of the algorithm. Thus, the control strategy can not only increase the active power output of the WF but also enable the WF to track the reactive power dispatching instruction of the power grid. A simulation of the DFIG WF is given to verify the effectiveness of the proposed control strategy at different wind speeds and reactive power references.

With the increasing consumption of fossil fuel, reducing greenhouse gas (GHG) emissions has become a serious issue that has attracted worldwide attention. Therefore, Lebanon is currently interested in utilizing renewable energy technologies to reduce energy dependence on oil reserves and GHG emissions. The present study is focused on solar and wind power potential and the economic viability of wind/solar systems for the Rayak region in Lebanon for the first time. The input data sources for the study including the Meteorological data for wind speed and NASA database for solar energy. In the assessment of wind energy, a two-parameter Weibull distribution function was used to analyze the characteristics of wind speed. Yearly and seasonal Weibull parameters were calculated for 10 m height using the Maximum likelihood method. In addition, yearly and seasonal wind power density values were calculated. The results showed that the mean annual wind speed and wind power density values were 5.884 m/s and 124.534 W/m2, respectively, during the investigation period. It can be concluded that the value of wind power density at the region was classified as marginal wind power potential and high-scale wind turbines can be used to gather wind energy potential in the region. Furthermore, predicting wind speed depends on various atmospheric factors and random variables. Therefore, 63 ANN models are developed by varying the meteorological parameters to predict the daily wind speed in the selected region. All the models with various combinations are validated and the performances of the models are analyzed using root mean squared error. The results demonstrated that the most relevant input variables for predicting the daily wind speed were found to be temperature, pressure, and relative humidity. In the assessment of wind energy, average monthly global solar radiation data were evaluated. It is found that the annual global solar radiation was 1877.41 kWh/m2, which indicates the selected region has high solar resources and is categorized as an excellent potential class. Moreover, this study provides a comprehensive and integrated feasibility analysis of 100 MW grid-connected wind and solar projects economic projects that can be developed in the country to reduce the electricity crisis and GHG emissions. Several different economic and financial indicators were calculated. The results indicate that the wind farm is a more economical option than the solar plant because of the higher values of NPV, BCR, ALCS, and IRR as well as the lower values of EB, SB, and LCOE.

ABSTRACTThe 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.

The goal of this study is to quantify the world's wind power potential for the first time from data. Wind speeds are calculated at 80 m, the hub height of modern, 77‐m diameter, 1500 kW turbines. Since relatively few observations are available at 80 m, the Least Square extrapolation technique is utilized and revised here to obtain estimates of wind speeds at 80 m given observed wind speeds at 10 m (widely available) and a network of sounding stations. Tower data from the Kennedy Space Center (Florida) were used to validate the results. Globally, ∼13% of all reporting stations experience annual mean wind speeds ≥ 6.9 m/s at 80 m (i.e., wind power class 3 or greater) and can therefore be considered suitable for low‐cost wind power generation. This estimate is believed to be conservative. Of all continents, North America has the largest number of stations in class ≥ 3 (453), and Antarctica has the largest percent (60%). Areas with great potential are found in northern Europe along the North Sea, the southern tip of the South American continent, the island of Tasmania in Australia, the Great Lakes region, and the northeastern and northwestern coasts of North America. The global average 10‐m wind speed over the ocean from measurements is 6.64 m/s (class 6); that over land is 3.28 m/s (class 1). The calculated 80‐m values are 8.60 m/s (class 6) and 4.54 m/s (class 1) over ocean and land, respectively. Over land, daytime 80‐m wind speed averages obtained from soundings (4.96 m/s) are slightly larger than nighttime ones (4.85 m/s); nighttime wind speeds increase, on average, above daytime speeds above 120 m. Assuming that statistics generated from all stations analyzed here are representative of the global distribution of winds, global wind power generated at locations with mean annual wind speeds ≥ 6.9 m/s at 80 m is found to be ∼72 TW (∼54,000 Mtoe) for the year 2000. Even if only ∼20% of this power could be captured, it could satisfy 100% of the world's energy demand for all purposes (6995–10177 Mtoe) and over seven times the world's electricity needs (1.6–1.8 TW). Several practical barriers need to be overcome to fully realize this potential.

Near‐surface seasonal and annual mean wind speed in Switzerland is investigated using homogenized observations, Twentieth Century Reanalysis (20CRv2c) data and raw model output of a 75 member EURO‐COoRdinated Downscaling EXperiment regional climate model (RCM) ensemble for present day and future scenarios. The wind speed observations show a significant decrease in the Alps and on the southern Alpine slopes in the period 1981–2010. However, the 20CRv2c data reveal that the recent trends lie well within the decadal variability over longer time periods and no clear signs of a systematic wind stilling can be found for Switzerland. The ensemble of RCMs shows large biases in the annual mean wind speed over the Jura mountains, and some members also show large biases in the Alps compared to station observations. The spatial distribution of the model biases varies strongly between the RCMs, while the resolution and the driving global model have less impact on the pattern of the model bias. The RCMs are mostly able to represent the seasonality of wind speed on the Plateau but miss important details in complex terrain related to local wind systems. Most models show no significant changes in near‐surface mean wind speed until the end of the 21st century. The model ensemble changes range from a 7% decrease to a 6% increase with an ensemble mean decrease of 1 to 2%. Due to model biases, the scale mismatch between model grid and station observations and the missing representation of local winds in the simulations, the changes need to be interpreted with utmost care. Future assessments might lead to major revisions even for the sign of the projected changes, in particular over complex terrain.

Recent wind farm studies have revealed elevated nighttime surface temperatures but have not validated physical mechanisms that create the observed effects. We report measurements of concurrent differences in surface wind speed, temperature, fluxes, and turbulence upwind and downwind of two turbine lines at the windward edge of a utility‐scale wind farm. On the basis of these measurements, we offer a conceptual model based on physical mechanisms of how wind farms affect their own microclimate. Periods of documented curtailment and zero‐power production of the wind farm offer useful opportunities to rigorously evaluate the microclimate impact of both stationary and operating turbines. During an 80 min nighttime wind farm curtailment, we measured abrupt and large changes in turbulent fluxes of momentum and heat leeward of the turbines. At night, wind speed decreases in the near wake when turbines are off but abruptly increases when turbine operation is resumed. Our measurements are compared with Moderate Resolution Imaging Spectroradiometer Terra and Aqua satellite measurements reporting wind farms to have higher nighttime surface temperatures. We demonstrate that turbine wakes modify surface fluxes continuously through the night, with similar magnitudes during the Terra and Aqua transit periods. Cooling occurs in the near wake and warming in the far wake when turbines are on, but cooling is negligible when turbines are off. Wind speed and surface stratification have a regulating effect of enhancing or decreasing the impact on surface microclimate due to turbine wake effects.