• Journal of Electrical Engineering and Technology
• /
• v.9 no.6
• /
• pp.1812-1821
• /
• 2014

Wind has been a rapidly growing renewable power source for the last twenty years. Since wind behavior is chaotic in nature, its forecasting is not easy. At the same time, developing an accurate forecasting method is essential when wind farms are integrated into the power grid. In fact, wind speed forecasting tools can solve issues related to grid stability and reserve allocation. In this paper 30 hours ahead wind speed profile forecast is proposed using Adaptive Wavelet Neural Network (AWNN). The implemented AWNN uses a Mexican hat mother Wavelet, and Morlet Mother Wavelet for seven, eight and nine levels decompositions. For wind speed forecasting, the time series data on wind speed has been gathered from the National Renewable Energy Laboratory (NREL) website. In this work, hourly averaged 10-min wind speed data sets for the year 2004 in the Midwest ISO region (site number 7263) is taken for analysis. Data sets are normalized in the range of [-1, 1] to improve the training performance of forecasting models. Total 8760 samples were taken for this forecasting analysis. After the forecasting phase, statistical parameters are calculated to evaluate system accuracy, comparing different configurations.

• Journal of Environmental Science International
• /
• v.18 no.8
• /
• pp.825-831
• /
• 2009

A first step review is completed on the suitability of European designed wind turbines in an East Asia climate. Six parameters are chosen for detailed analysis of proper meteorological measures from flat, hilly, forested, coastal and offshore sites in West Europe and East Asia: mean wind speed, 10 minute mean wind speed distribution, turbulence intensity, wind shear, 3 second extreme wind speed and 10 minute direction change. All six parameters are assessed with a view for contrast with the wind turbine design standard IEC61400. The diurnal and seasonal variation, average and extreme values of each parameter are calculated where appropriate. Industry standard software and analysis techniques have been employed to assess the applicability of existing wind turbine design standards and design guidelines for the East Asian market.

• Smart Structures and Systems
• /
• v.21 no.5
• /
• pp.601-609
• /
• 2018

The estimated probabilistic model of wind data based on the conventional approach may have high discrepancy compared with the true distribution because of the uncertainty caused by the instrument error and limited monitoring data. A sequential quadratic programming (SQP) algorithm-based finite mixture modeling method has been developed in the companion paper and is conducted to formulate the joint probability density function (PDF) of wind speed and direction using the wind monitoring data of the investigated bridge. The established bivariate model of wind speed and direction only represents the features of available wind monitoring data. To characterize the stochastic properties of the wind parameters with the subsequent wind monitoring data, in this study, Bayesian inference approach considering the uncertainty is proposed to update the wind parameters in the bivariate probabilistic model. The slice sampling algorithm of Markov chain Monte Carlo (MCMC) method is applied to establish the multi-dimensional and complex posterior distribution which is analytically intractable. The numerical simulation examples for univariate and bivariate models are carried out to verify the effectiveness of the proposed method. In addition, the proposed Bayesian inference approach is used to update and optimize the parameters in the bivariate model using the wind monitoring data from the investigated bridge. The results indicate that the proposed Bayesian inference approach is feasible and can be employed to predict the bivariate distribution of wind speed and direction with limited monitoring data.

• Wind and Structures
• /
• v.17 no.4
• /
• pp.419-433
• /
• 2013

This paper investigates the vertical profiles of horizontal mean wind speed and direction based on the synchronized measurements from a Doppler radar profiler and an anemometer during 16 tropical cyclones at a coastal site in Hong Kong. The speed profiles with both open sea and hilly exposures were found to follow the log-law below a height of 500 m. Above this height, there was an additional wind speed shear in the profile for hilly upwind terrain. The fitting parameters with both the power-law and the log-law varied with wind strength. The direction profiles were also sensitive to local terrain setups and surrounding topographic features. For a uniform open sea terrain, wind direction veered logarithmically with height from the surface level up to the free atmospheric altitude of about 1200 m. The accumulated veering angle within the whole boundary layer was observed to be $30^{\circ}$. Mean wind direction under other terrain conditions also increased logarithmically with height above 500 m with a trend of rougher exposures corresponding to lager veering angles. A number of empirical parameters for engineering applications were presented, including the speed adjustment factors, power exponents of speed profiles, and veering angle, etc. The objective of this study aims to provide useful information on boundary layer wind characteristics for wind-resistant design of high-rise structures in coastal areas.

• Wind and Structures
• /
• v.31 no.5
• /
• pp.419-430
• /
• 2020

Weibull distribution is a conspicuous distribution known for its accuracy and its usage for wind energy analysis. The two and three parameter Weibull distributions are adopted in this study to fit wind speed data. The daily mean wind speed data of Ennore, Tamil Nadu, India has been used to validate the procedure. The parameters are estimated using maximum likelihood method, least square method and moment method. Four statistical tests namely Root mean square error, R2 test, Kolmogorov-Smirnov test and Anderson-Darling test are employed to inspect the fitness of Weibull probability density functions. The value of shape factor, scale factor, wind speed and wind power are determined at a height of 100m using extrapolation of numerical equations. Also, the value of capacity factor is calculated mathematically. This study provides a way to evaluate feasible locations for wind energy assessment, which can be used at any windy site throughout the world.

• KSCE Journal of Civil and Environmental Engineering Research
• /
• v.28 no.2A
• /
• pp.197-205
• /
• 2008

In order to estimate mean wind speed in the Gwangyang-Myodo Region, the probability distribution model of extreme values has been used in the statistical analysis of joint distribution probability of daily maximum wind speed and corresponding direction in this paper. For this purpose frequency of daily maximum records at respective stations is inquired into and sample of largest yearly wind speed of sixteen compass direction and non-direction is extracted from daily data of maximum wind speed and appropriate direction of the meteorological observing stations nearby the bridge construction site. These extreme speed records are applied to Gumbel and Weibull distribution model and parameters are estimated through method of moment and method of least squares etc. And also, distribution and parameters are inquired into whether it is fitted through the probability plot correlation coefficient examination. From fitted parameters the largest yearly wind speed of sixteen compass direction and non-direction is extrapolated taking into account factors regarding sample size of data and distance from the bridge construction site according to the appropriate stations.

• Journal of Electrical Engineering and Technology
• /
• v.11 no.6
• /
• pp.1548-1555
• /
• 2016

This paper presents an application of time series analysis in hourly wind speed simulation and forecast in Jeju Island, Korea. Autoregressive - moving average (ARMA) model, which is well in description of random data characteristics, is used to analyze historical wind speed data (from year of 2010 to 2012). The ARMA model requires stationary variables of data is satisfied by power law transformation and standardization. In this study, the autocorrelation analysis, Bayesian information criterion and general least squares algorithm is implemented to identify and estimate parameters of wind speed model. The ARMA (2,1) models, fitted to the wind speed data, simulate reference year and forecast hourly wind speed in Jeju Island.

• International Journal of Fluid Machinery and Systems
• /
• v.2 no.1
• /
• pp.92-101
• /
• 2009

The purpose of this paper is to gain a greater understanding of the performance of practical wind turbine generating systems with differing output power controllers and controlling means for wind turbine speed. Subjected wind turbines, both equipped with an asynchronous power generator, are located at two sites and are defined as wind turbine A and wind turbine B in this study, respectively. Their performance differences are examined by measuring wind speed and electric parameters. The study suggests that both wind turbines have a clear linkage between current and output power fluctuations. Comparison of the fluctuations to wind speed fluctuation, although they are triggered primarily by wind speed fluctuation, clearly indicates the specific behaviors inherent to the respective turbine control mechanisms.

• Wind and Structures
• /
• v.23 no.4
• /
• pp.351-366
• /
• 2016

Nowadays, wind energy is the most rapidly developing technology and energy source and it is reusable. Due to its cleanliness and reusability, there have been rapid developments made on transferring the wind energy systems to electric energy systems. Converting the wind energy to electrical energy can be done only with the wind turbines. So installing a wind turbine depends on the wind speed at that location. The expected wind power can be estimated using a perfect probability distribution. In this paper Weibull and Weibull distribution with multiple parameters has been used in deriving the mathematical expression for estimating the wind power. Statistically the parameters of Weibull and Weibull distribution are estimated using the maximum likelihood techniques. We derive a probability distribution for the power output of a wind turbine with given rated wind speeds for the regions where the wind speed histograms present a bimodal pdf and compute the first order moment of this distribution.

• Wind and Structures
• /
• v.34 no.6
• /
• pp.469-482
• /
• 2022

The generalized extreme value distribution (GEVD) is frequently used to fit the block maximum of environmental parameters such as the annual maximum wind speed. There are several methods for estimating the parameters of the GEV distribution, including the least-squares method (LSM). However, the application of the LSM with the expected order statistics has not been reported. This study fills this gap by proposing a fitting method based on the expected order statistics. The study also proposes a plotting position to approximate the expected order statistics; the proposed plotting position depends on the distribution shape parameter. The use of this approximation for distribution fitting is carried out. Simulation analysis results indicate that the developed fitting procedure based on the expected order statistics or its approximation for GEVD is effective for estimating the distribution parameters and quantiles. The values of the probability plotting correlation coefficient that may be used to test the distributional hypothesis are calculated and presented. The developed fitting method is applied to extreme thunderstorm and non-thunderstorm winds for several major cities in Canada. Also, the implication of using the GEVD and Gumbel distribution to model the extreme wind speed on the structural reliability is presented and elaborated.