Two and three-parameter Weibull distribution in available wind power analysis

Wais, Piotr

doi:10.1016/j.renene.2016.10.041

Cited by 174 publications

(66 citation statements)

References 26 publications

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“…There are several methods that can be applied to analyze the wind data for estimating the wind potential [13,14] for a particular region. It was found from the literature that Weibull and Rayleigh [15] are the most preferred methods for determining the wind energy potential. In order to implement the Weibull and Rayleigh distribution, we need to estimate the shape and scale parameter.…”

Section: Distribution Characteristics For Wind Datamentioning

confidence: 99%

Wind Energy Potential Assessment by Weibull Parameter Estimation Using Multiverse Optimization Method: A Case Study of Tirumala Region in India

et al. 2019

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In this paper the multiverse optimization (MVO) was used for estimating Weibull parameters. These parameters were further used to analyze the wind data available at a particular location in the Tirumala region in India. An effort had been made to study the wind potential in this region (13 • 41 30.4" N 79 • 21 34.4" E) using the Weibull parameters. The wind data had been measured at this site for a period of six years from January 2012 to December 2017. The analysis was performed at two different hub heights of 10 m and 65 m. The frequency distribution of wind speed, wind direction and mean wind speeds were calculated for this region. To compare the performance of the MVO, gray wolf optimizer (GWO), moth flame optimization (MFO), particle swarm optimization (PSO) and other numerical methods were considered. From this study, the performance had been analyzed and the best results were obtained by using the MVO with an error less than one. Along with the Weibull frequency distribution for the selected region, wind direction and wind speed were also provided. From the analysis, wind speed from 2 m/s to 10 m/s was present in sector 260-280 • and wind from 0-4 m/s were present in sector 170-180 • of the Tirumala region in India.optimization is that there exists information exchange among the solutions of candidates. In this way they can handle the local optima, bias of search space and premature convergence easier and faster. Some of the meta-heuristic optimization such as the genetic algorithm (GA), particle swarm optimization (PSO), ant colony optimization (ACO) and differential evolution algorithm (DEA) have overcome the limitations of single solution optimization methods. Therefore, in this paper a multiverse optimization (MVO) technique is implemented to estimate the parameters to adjust and fit the actual wind profile. This methodology serves as an innovative solution for any particular wind conditions provided a well-defined pattern has been provided. The MVO outperforms other optimization techniques and is being applied for wind energy applications to achieve rapid convergence in estimating Weibull parameters. This MVO algorithm is compared with PSO, which is best among the SI based technique and GWO, MFO as one of the most recent algorithms. The proposed algorithm has high exploitation ability due to combination of WEP/TDR constants and wormholes combined to provide high exploitation. Superior exploration of the proposed algorithm is due to white and black holes to exchange different objects.Here, the wind data for a period of six years from January 2012 to December 2017 in the Tirumala region (13 • 41 30.4" N 79 • 21 34.4" E) is being studied which is located in the southern part of India. In order to analyze the wind distribution, scale and shape parameters are determined using MVO. This gives two values, which is further utilized to determine the probability density function of the Weibull and Rayleigh distribution. With this study it has been estimated that there is sufficient wind potential in this region...

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Section: Distribution Characteristics For Wind Datamentioning

confidence: 99%

Wind Energy Potential Assessment by Weibull Parameter Estimation Using Multiverse Optimization Method: A Case Study of Tirumala Region in India

et al. 2019

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“…Due to its extremely high flexibility, the Weibull distribution [1] is widely used for fitting engineering data, such as the strength of materials [2,3], fracture of brittle materials [4,5], and wind speed [6,7]. The three-parameter Weibull distribution is very flexible for random data fitting so that it has a strong adaptability for different types of probability distribution.…”

Section: Introductionmentioning

confidence: 99%

Maximum Likelihood Estimation for Three-Parameter Weibull Distribution Using Evolutionary Strategy

Yang

Ren

2019

Mathematical Problems in Engineering

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The maximum likelihood estimation is a widely used approach to the parameter estimation. However, the conventional algorithm makes the estimation procedure of three-parameter Weibull distribution difficult. Therefore, this paper proposes an evolutionary strategy to explore the good solutions based on the maximum likelihood method. The maximizing process of likelihood function is converted to an optimization problem. The evolutionary algorithm is employed to obtain the optimal parameters for the likelihood function. Examples are presented to demonstrate the proposed method. The results show that the proposed method is suitable for the parameter estimation of the three-parameter Weibull distribution.

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“…Fial et al successfully applied two‐parameter and three‐parameter Weibull models for reliability estimation of structural steels. Wais examined two and three parameter Weibull models to calculate availability and reliability of turbines in wind power industries, demonstrating that application of the two‐parameter Weibull model could enhance system reliability. Furthermore, the two‐parameter Weibull model was found to be useful for the performance analysis of redundant systems and can provide maintenance strategies when the random variable follows the Weibull distribution .…”

Section: Introductionmentioning

confidence: 99%

Reliability modelling with redundancy—A case study of power generation engines in a wastewater treatment plant

Jolfaei

Jin

Linden

et al. 2019

Quality & Reliability Eng

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Power generators are critical assets in wastewater treatment plants (WWTPs) in Australia and many countries. Better managing the lifetime, minimising failures, improving reliability and availability, and reducing operating and maintenance costs of the power generation assets are still challenging topics for water utilities. This case study aims to develop power generation system reliability and availability modelling considering redundancy to minimise operation and maintenance costs. The two‐parameter Weibull model was used to assess system reliability and availability to power generation engines in WWTPs. The Kaplan‐Meier method (a time‐driven estimation technique) and the log beta‐Weibull model (which is suitable for modelling censored and uncensored data) were used to analyse and validate the modelling results. Shape and scale parameters of the Weibull models were estimated by maximising the log‐likelihood function using non‐linear optimisation. Hazard and reliability functions were calculated using the Weibull model. Results using two‐parameter Weibull, Kaplan‐Meier, and log beta‐Weibull models display low reliability and high hazard rate over time, which was associated with spark plug failure due to a suboptimal start and stop operation strategy.

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Two and three-parameter Weibull distribution in available wind power analysis

Cited by 174 publications

References 26 publications

Wind Energy Potential Assessment by Weibull Parameter Estimation Using Multiverse Optimization Method: A Case Study of Tirumala Region in India

Wind Energy Potential Assessment by Weibull Parameter Estimation Using Multiverse Optimization Method: A Case Study of Tirumala Region in India

Maximum Likelihood Estimation for Three-Parameter Weibull Distribution Using Evolutionary Strategy

Reliability modelling with redundancy—A case study of power generation engines in a wastewater treatment plant

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