• 제목/요약/키워드: Weibull Shape Factor

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Bayesian Model Selection in Weibull Populations

  • Kang, Sang-Gil
    • Journal of the Korean Data and Information Science Society
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    • 제18권4호
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    • pp.1123-1134
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    • 2007
  • This article addresses the problem of testing whether the shape parameters in k independent Weibull populations are equal. We propose a Bayesian model selection procedure for equality of the shape parameters. The noninformative prior is usually improper which yields a calibration problem that makes the Bayes factor to be defined up to a multiplicative constant. So we propose the objective Bayesian model selection procedure based on the fractional Bayes factor and the intrinsic Bayes factor under the reference prior. Simulation study and a real example are provided.

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Default Bayesian testing for the equality of shape parameters in the inverse Weibull distributions

  • Kang, Sang Gil
    • Journal of the Korean Data and Information Science Society
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    • 제25권6호
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    • pp.1569-1579
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    • 2014
  • This article deals with the problem of testing for the equality of the shape parameters in two inverse Weibull distributions. We propose Bayesian hypothesis testing procedures for the equality of the shape parameters under the noninformative prior. The noninformative prior is usually improper which yields a calibration problem that makes the Bayes factor to be defined up to a multiplicative constant. So we propose the default Bayesian hypothesis testing procedures based on the fractional Bayes factor and the intrinsic Bayes factors under the reference priors. Simulation study and an example are provided.

와이블 형상계수에 따른 이용률 변화 (Variation of Capacity Factors by Weibull Shape Parameters)

  • 권일한;김진한;백인수;유능수
    • 한국태양에너지학회 논문집
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    • 제33권1호
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    • pp.32-39
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    • 2013
  • Effects of Weibull shape parameter, k, on capacity factors of wind turbines were investigated. Wind distributions with mean wind speeds of 5 m/s, 6 m/s, 7 m/s and 8 m/s were simulated and used to estimate the annual energy productions and capacity factors of a 2MW wind turbine for various Weibull shape parameters. It was found from the study that the capacity factors of wind turbines are much affected by Weibull shape parameters. When the annual mean wind speed at the hub height of a wind turbine was about 7 m/s, and the air density was assumed to be 1.225 $kg/m^3$, the maximum capacity factor of a 2 MW wind turbine having a rated wind speed of 13 m/s was found to occur with the shape parameter of 2. It was also found that as the mean wind speed increased, the Weibull k parameter which yielded the maximum capacity factor increased. The simulated results were also validated by predictions of capacity factors of wind turbines using wind data measured in complex terrain.

The Fractional Bayes Factor Approach to the Bayesian Testing of the Weibull Shape Parameter

  • Cha, Young-Joon;Cho, Kil-Ho;Cho, Jang-Sik
    • Journal of the Korean Data and Information Science Society
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    • 제17권3호
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    • pp.927-932
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    • 2006
  • The techniques for selecting and evaluating prior distributions are studied over recent years which the primary emphasis is on noninformative priors. But, noninformative priors are typically improper so that such priors are defined only up to arbitrary constants which affect the values of Bayes factors. In this paper, we consider the Bayesian hypotheses testing for the Weibull shape parameter based on fractional Bayes factor which is to remove the arbitrariness of improper priors. Also we present a numerical example to further illustrate our results.

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Mathematical representation to assess the wind resource by three parameter Weibull distribution

  • Sukkiramathi, K.;Rajkumar, R.;Seshaiah, C.V.
    • Wind and Structures
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    • 제31권5호
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    • pp.419-430
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    • 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.

확률밀도함수를 이용한 멤브레인방식 LNG탱크의 선형누적손상도 평가에 관한 연구 (A Study on the Evaluation of Linear Cumulative Damage Factor of Membrane Type LNG Tank by use of Probability Density Function)

  • 김종호
    • Journal of Advanced Marine Engineering and Technology
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    • 제28권6호
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    • pp.993-999
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    • 2004
  • The estimation of fatigue life at the design stage of membrane type LNG tank is very important in order to arrive at feasible and cost effective solutions considering the total lifetime of the tank. In this study, the practical procedure of fatigue life prediction by use of cumulative damage factors based on Miner-Palmgren hypothesis and probability density function has been shown with the corner region of Gaz Transport Membrane type LNG tank being used as an example. In particular the parameters of Weibull distribution that determine the stress spectrum are discussed. The main results obtained from this study are as follows: 1. The recommended value for the shape parameter of Weibull distribution for the LNG tank is 1.1 in case of using the direct calculation method proposed in this study. 2. The calculated fatigue life is influenced by the shape parameter of Weibull distribution and stress block. The safe fatigue design can be achieved by using higher value of shape parameter and the stress blocks divided into more stress blocks.

A bimodal Weibull distribution - capacity factor for different heights at sulur

  • Seshaiah, C.V.;Indhumathy, D.
    • Wind and Structures
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    • 제28권1호
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    • pp.63-70
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    • 2019
  • Due to developing environmental concern use of renewable energy source is very essential. The great demand for the energy supply coupled with inadequate energy sources creates an emergency to find a new solution for the energy shortage. The appropriate wind energy distribution is the fundamental requirement for the assessment of wind energy potential available at the particular site essential for the design of wind farms. Hence the proper specification of the wind speed distribution plays a vital role. In this paper the Bimodal Weibull distribution is used to estimate the Capacity factor at the proposed site. The shape and scale parameters estimated using Maximum likelihood method is used as the initial value for extrapolation. Application of this model will give an accurate result overwhelming the concept of overestimation or underestimation of Capacity factor.

대관령 풍력단지의 풍력발전량 및 경제성 분석 (A Study on the Wind rower Generation and Its Economic Feasibility at Daekwanryung)

  • 하정우;김수덕
    • 에너지공학
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    • 제14권2호
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    • pp.123-132
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    • 2005
  • 본 논문은 현재 건설 중인 대관령 풍력발전 단지의 풍력 발전 관련 풍속의 분포 및 풍력 발전량을 검토하고, 대관령 풍력 발전의 경제성을 분석하였다. 풍력발전에 있어 풍속의 분포가 핵심요소이고, 풍속분포는 shape factor및 scale factor로 구성되는 Weibull 분포함수로 일반적으로 표현되나, 실제 풍속 분포자료를 조사하여 최적의 계수를 도출, 연간 풍력 발전량을 구하였다. 풍력발전 시설의 초기투자비 및 유지관리 비용은 국내 자료를 이용코자 하였으나, 관련 자료의 확보에 어려움이 있어 유럽풍력협회의 자료(EWEA, 2003. 12)를 적용하였다. 결과는 현재 발전차액제도를 통해 보전되는 풍력에너지의 적정한 기준가격을 마련하는데 유익한 정보를 제공한다.

Hypotheses Testing for the Shape Parameter of the Weibull Lifetime Data

  • Kang, Sang-Gil;Kim, Dal-Ho;Cho, Jang-Sik
    • 품질경영학회지
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    • 제27권4호
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    • pp.153-166
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    • 1999
  • In this paper, we address the Bayesian hypotheses testing for the shape parameter of weibull model. In Bayesian testing problem, conventional Bayes factors can not typically accommodate the use of noninformative priors which are improper and are defined only up to arbitrary constants. To overcome such problem, we use the recently proposed hypotheses testing criterion called the intrinsic Bayes factor. We derive the arithmetic and median intrinsic Bayes factors and use these results to analyze real data sets.

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