• 대한전기학회:학술대회논문집
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• 대한전기학회 2002년도 하계학술대회 논문집 B
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• pp.1338-1341
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• 2002

The Weibull probability density function and the Rayleigh function are compared by analyzing the relations of the capacity factors which are compared the actual wind speed frequency curve with which are modelled using the probability density functions with different mean wind speeds. For this analysis, the wind speed means of arithmetic, root mean square, cubic mean cuberoot, and standard deviations are computed from the measured wind speed data of a specific site and the coefficients of probability density functions are calculated. The capacity factors for Vestas 850[kW] wind turbine are calculated and analyzed. The results shows that the wind speed frequency curve by Rayleigh function is more close to the actual curve than by Weibull function. The more the wind speed frequency curve is close to the actual one, the more the capacity factors become large values.

• Wind and Structures
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• 제10권1호
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• pp.45-60
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• 2007

Transmission tower is a vital component in electrical system. In order to accurately compute the dynamic response and reliability of transmission tower under the excitation of wind loading, a new method termed as probability density evolution method (PDEM) is introduced in the paper. The PDEM had been proved to be of high accuracy and efficiency in most kinds of stochastic structural analysis. Consequently, it is very hopeful for the above needs to apply the PDEM in dynamic response of wind-excited transmission towers. Meanwhile, this paper explores the wind stochastic field from stochastic Fourier spectrum. Based on this new viewpoint, the basic random parameters of the wind stochastic field, the roughness length $z_0$ and the mean wind velocity at 10 m heigh $U_{10}$, as well as their probability density functions, are investigated. A latticed steel transmission tower subject to wind loading is studied in detail. It is shown that not only the statistic quantities of the dynamic response, but also the instantaneous PDF of the response and the time varying reliability can be worked out by the proposed method. The results demonstrate that the PDEM is feasible and efficient in the dynamic response and reliability analysis of wind-excited transmission towers.

• 풍력에너지저널
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• 제14권1호
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• pp.37-43
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• 2023

In this study, synthetic time series wind data was generated numerically using a second-order Markov chain. One year of wind data in 2020 measured by the AWS on Wido Island was used to investigate the statistics for measured wind data. Both the transition probability matrix and the cumulative transition probability matrix for annual hourly mean wind speed were obtained through statistical analysis. Probability density distribution along the wind speed and autocorrelation according to time were compared with the first- and the second-order Markov chains with various lengths of time series wind data. Probability density distributions for measured wind data and synthetic wind data using the first- and the second-order Markov chains were also compared to each other. For the case of the second-order Markov chain, some improvement of the autocorrelation was verified. It turns out that the autocorrelation converges to zero according to increasing the wind speed when the data size is sufficiently large. The generation of artificial wind data is expected to be useful as input data for virtual digital twin wind turbines.

• 한국태양에너지학회 논문집
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• 제30권2호
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• pp.54-64
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• 2010

Interest in new and renewable energies like solar energy and wind energy is increasing throughout the world due to the rapidly expanding energy consumption and environmental reasons. An essential requirement for wind force power generation is estimating the size of wind energy accurately. Wind energy is estimated usually using meteorological data or field measurement. This study attempted to estimate wind energy density using meteorological data on daily mean wind speed and the Weibull parameters in Seoul, a representative inland city where over 60% of 15 story or higher apartments in Korea are situated, and Busan, Incheon, Ulsan and Jeju that are major coastal cities in Korea. According to the results of analysis, the monthly mean probability density distribution based on the daily mean wind speed agreed well with the monthly mean probability density distribution based on the Weibull parameters. This finding suggests that the Weibull parameters, which is highly applicable and convenient, can be utilized to estimate the wind energy density distribution of each area. Another finding was that wind energy density was higher in coastal cities Busan and Incheon than in inland city Seoul.

• Wind and Structures
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• 제37권3호
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• pp.211-227
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• 2023

To investigate the non-Gaussian feature of fluctuating wind pressures on rectangular high-rise buildings, wind tunnel tests were conducted on scale models with side ratios ranging from 1/9~9 in an open exposure for various wind directions. The high-order statistical moments, time histories, probability density distributions, and peak factors of pressure fluctuations are analyzed. The mixed normal-Weibull distribution, Gumbel-Weibull distribution, and lognormal-Weibull distribution are adopted to fit the probability density distribution of different non-Gaussian wind pressures. Zones of Gaussian and non-Gaussian are classified for rectangular buildings with various side ratios. The results indicate that on the side wall, the non-Gaussian wind pressures are related to the distance from the leading edge. Apart from the non-Gaussianity in the separated flow regions noted by some literature, wind pressures behind the area where reattachment happens present non-Gaussian nature as well. There is a new probability density distribution type of non-Gaussian wind pressure which has both long positive and negative tail found behind the reattachment regions. The correlation coefficient of wind pressures is proved to reflect the non-Gaussianity and a new method to estimate the mean reattachment length of rectangular high-rise building side wall is proposed by evaluating the correlation coefficient. For rectangular high-rise buildings, the mean reattachment length calculated by the correlation coefficient method along the height changes in a parabolic shape. Distributions of Gaussian and non-Gaussian wind pressures vary with side ratios. It is inappropriate to estimate the extreme loads of wind pressures using a fixed peak factor. The trend of the peak factor with side ratios on different walls is given.

• 한국측량학회지
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• 제30권3호
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• pp.295-303
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• 2012

풍력발전 단지의 수익성 평가를 위해 연간 에너지 생산량(AEP ; Annual Energy Production)의 계산이 중요하다. AEP를 계산하기 위해서는 바람의 확률밀도함수(PDF ; Probability Density Function)와 풍력발전기의 발전곡선(PC; Power Curve)이 필요하며, AEP 예측의 정확성을 향상시키기 위해서는 허브 높이에서의 PDF예측과 그 높이의 공기밀도에 따른 풍력발전기 PC의 결정이 중요하다. 본 연구에서는 제주도 한동, 평대의 실관측 풍황탑(met mast) 자료를 이용하였으며 풍속의 PDF를 Weibull 분포 함수로 가정 하였고 Weibull 함수의 파라미터의 값이 높이에 따라 변화하는 양상을 확인하였다. Weibul 함수의 계산은 모멘트법과 LN-least법을 사용하였으며, 모멘트법과 LN-least법에 의한 형상계수의 경우 높이의 증가에 따라 변화를 보이지 않았고 평균값에서 ${\pm}0.1$의 변화 패턴을 보였다. 척도계수의 경우 높이가 증가함에 따라 선형적으로 증가하였으며 지형별 분류에 따른 높이별 척도계수의 기울기는 확연한 차이를 보이고 있었다. 60m 높이에서 관측된 바람의 상대도수와 관측 값의 높이 보정에 의한 공기밀도와 일반식에 의한 공기밀도를 각각 계산하여 그 결과에 대응하는PC를 선택하여 AEP차이를 계산하였다.

• 한국태양에너지학회 논문집
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• 제33권2호
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• pp.18-27
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• 2013

This paper presents a method for assessing the wind energy potential at complex terrain using probability distribution. And the proper probability models of the parameters estimating the wind energy are presented. Finally a mixture-Weibull determined by numerical methods procedure are proposed to assess the probability distribution of the energy potential at a site. The developed method is applied to the Kwanjungchun Bridge and compared with wind records which the neighboring weather station.

• Wind and Structures
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• 제4권1호
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• pp.73-84
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• 2001

Analysis of pressures measured on the roof of the full-scale Texas Tech building and a 1/50 scale model of a typical house showed that the pressure fluctuations on cladding fastener and cladding-truss connection tributary areas have similar characteristics. The probability density functions of pressure fluctuations on these areas are negatively skewed from Gaussian, with pressure peak factors less than -5.5. The fluctuating pressure energy is mostly contained at full-scale frequencies of up to about 0.6 Hz. Pressure coefficients, $C_p$ and local pressure factors, $K_l$ given in the Australian wind load standard AS1170.2 are generally satisfactory, except for some small cladding fastener tributary areas near the edges.

• Wind and Structures
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• 제33권6호
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• pp.423-435
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• 2021

Although existing algorithms can predict wind speed using historical observation data, for engineering feasibility, most use moment methods and probability density functions to estimate fitted parameters. However, extreme wind speed prediction accuracy for long-term return periods is not always dependent on how the optimized frequency distribution curves are obtained; long-term return periods emphasize general distribution effects rather than marginal distributions, which are closely related to potential extreme values. Moreover, there are different wind speed parent sample types; how to theoretically select the proper extreme value distribution is uncertain. The influence of different sampling time intervals has not been evaluated in the fitting process. To overcome these shortcomings, updated steps are introduced, involving parameter sensitivity analysis for different sampling time intervals. The extreme value prediction accuracy of unknown parent samples is also discussed. Probability analysis of mean wind is combined with estimation of the probability plot correlation coefficient and the maximum likelihood method; an iterative estimation algorithm is proposed. With the updated steps and comparison using a Monte Carlo simulation, a fitting policy suitable for different parent distributions is proposed; its feasibility is demonstrated in extreme wind speed evaluations at Longhua and Chuansha meteorological stations in Shanghai, China.

• Wind and Structures
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• 제36권4호
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• pp.249-261
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• 2023

Aerodynamic force coefficients are generally prescribed by an ensemble average of ten and/or twenty 10-minute samples. However, this makes it difficult to identify the exact probability distribution and exceedance probability of the prescribed values. In this study, 12,600 10-minute samples on three tall buildings were measured, and the probability distributions were first identified and the aerodynamic force coefficients corresponding to the specific non-exceedance probabilities (cumulative probabilities) of wind load were then evaluated. It was found that the probability distributions of the mean and fluctuating aerodynamic force coefficients followed a normal distribution. The ratios of aerodynamic force coefficients corresponding to the specific non-exceedance probabilities (C_f,Non) to the ensemble average of 12,600 samples (C_f,Ens), which was defined as an adjusting factor (C_f,Non/C_f,Ens), were less than 2%. The effect of coefficient of variation of wind speed on the adjusting factor is larger than that of the annual non-exceedance probability of wind load. The non-exceedance probabilities of the aerodynamic force coefficient is between P_C,nonex = 50% and 60% regardless of force components and aspect ratios. The adjusting factors from the Gumbel distribution were larger than those from the normal distribution.