Assessing different parameters estimation methods of Weibull distribution to compute wind power density

Mohammadi, Kasra; Alavi, Omid; Mostafaeipour, Ali; Goudarzi, Navid; Jalilvand, Mahdi

doi:10.1016/j.enconman.2015.11.015

Cited by 251 publications

(136 citation statements)

References 59 publications

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“…In order to calculate the power law exponent, at least two wind speed measurements at different heights are required but the measurements at a single height were only used in this study. Thus, Equation (20) could not be directly applied for extrapolation in this study. However, another way of handling power law method was suggested by Justus and Mikhail [33].…”

Section: Wind Resource Extrapolationmentioning

confidence: 99%

“…The different points compared with existing studies are as follows: firstly, more actual wind speed data for the longer-term period of time were analyzed to improve the reliability of the results following each method's accuracy test. The measurement datasets in one to five sites covering the period of one to three years were usually analyzed in the previous studies [13][14][15][16][17][18][19][20][21], while the datasets in nine sites for five years were analyzed in this investigation. Secondly, although the topographical conditions were known as a major factor influencing wind conditions, it has been little discussed in terms of the accuracy of the methods.…”

Section: Introductionmentioning

confidence: 99%

“…Mohammadi et al [20] analyzed the wind data from four stations in Canada to determine the most appropriate Weibull parameters estimation method for computing wind power density applying each of the six aforementioned methods: the empirical (Justus), empirical (Lysen), graphical, MLM, MMLM, and energy pattern factor methods. On the whole, the energy pattern factor and empirical (Justus) methods were very favorable while the graphical method was shown to be the worst.…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Comparative Study of Different Methods for Estimating Weibull Parameters: A Case Study on Jeju Island, South Korea

Kang

Huh

2018

Energies

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On Jeju Island, South Korea, an investigation was conducted to determine the best method for estimating Weibull parameters. Six methods commonly used in many fields of the wind energy industry were reviewed: the empirical, moment, graphical, energy pattern factor, maximum likelihood, and modified maximum likelihood methods. In order to improve the reliability of a research result, five-year actual wind speed data taken from nine sites with various topographical conditions were used for the estimation. Furthermore, the effect of various topographical conditions on the accuracy of the methods was analyzed and 10 bin interval types were applied to determine the most appropriate bin interval based on their performances. Weibull distributions that were estimated using these methods were compared with the observed wind speed distribution. Then the accuracy of each method was evaluated using four accuracy tests. The results showed that of the six methods, the moment method had the best performance regardless of topographical conditions, while the graphical method performed the worst. Additionally, topographical conditions did not affect the accuracy ranking of the methods for estimating the Weibull parameters, while an increase of terrain complexity resulted in an increase of discrepancy between the estimated Weibull distribution and the frequency of the observed wind speed data. In addition, the choice in bin interval greatly affected the accuracy of the graphical method while it did not depend on the accuracy of the modified maximum likelihood method.

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Section: Wind Resource Extrapolationmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Comparative Study of Different Methods for Estimating Weibull Parameters: A Case Study on Jeju Island, South Korea

Kang

Huh

2018

Energies

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“…For example,Weibull Statistical Distribution is a prevalent method for examining wind speed measurements and specifying wind energy potential. Weibull probability density function can be used to predict wind density, wind energy potential and wind speed [19]- [20]. The continuous random variable x has a Weibull distribution, with parameters α and β, if its density function is given by…”

Section: Weibull Distributionmentioning

confidence: 99%

Sensor Localization Using Fixed and Dynamic Communication Ranges in Different Types of Distributed Sensor Networks

Karagöl¹,

Yıldız²,

Pathak³

2016

International Journal of Applied Mathematics, Electronics and Computers

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Wireless Sensor Network (WSN) refers to a group of locationally dispensed and dedicated sensors for observing and recording the physical conditions of the environment and coordinating the aggregated data at a centrical location. To serve such new applications, localization is largely used in WSNs to define the current location of the sensor nodes. Time of Arrival (ToA) localization is one of the prevalent schemes due to its high estimation accuracy. ToA is a method to estimate the location of a target based on the correlation of the signals and calculating the distances from each anchor to the target by multiplying the speed of light and the time at which the signal is received. In our recent study, we propose Modified 3N algorithm in 2D space. In the Modified 3N algorithm in 2D, three circles were used to localize the target nodes in the network. In this paper; Uniform, Beta, Weibull, Gamma and Generalized Pareto distributed networks are used for localization with the Modified 3N algorithm in 2D and the localization performance of the networks are evaluated and compared using MATLAB simulations. For these simulations, firstly, constant communication range of 10% of the field dimension is used and then dynamic communication ranges that depend on the number of total nodes are used for the same areas.

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“…Traditionally Weibull distribution is widely used to represent wind speed series at a given site [4,[11][12][13]. Normal, Gamma, Lognormal or combination of these distributions with Weibull distribution [4,7,[14][15][16], empirical wavelet [17] or Kernel density method [18] can also be used to model wind speed series.…”

Section: Introductionmentioning

confidence: 99%

Characterisation of wind speed series and power in Durban

Legesse¹,

Saha²,

Carpanen³

2017

J. energy South. Afr.

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Both the planning and operating of a wind farm demand an appropriate wind speed model of its location. The model also helps predict the dynamic behaviour of wind turbines and wind power potential in the location. This study characterises the wind speed series and power in Durban (29.9560°S, 30.9730°E) IntroductionDepletion of fossil fuel reserves, global warming, security concerns, and rising commodity prices are pushing the world to go green. Much attention is currently given to the development of renewable energy, among which harnessing wind energy is the cheapest alternative [1][2][3]. Feasibility studies related to wind power require an appropriate wind speed model of a site [4,5]. These models are also important in planning and operating wind turbines. Hence, the wind speeds of a specified site should be appropriately characterised to determine wind energy potential and attain comprehensive results in the investigations of the dynamics of the wind turbines [4,6,7]. Moreover, a good characterisation of wind speed helps transmission system operators in scheduling their power dispatch [4]. Wind is a random stochastic process whose dynamic behaviour can be represented by a stochastic model [8]. Naturally, it depends on pressure gradient, waves, jet streams, and local weather conditions [9]. Its stochastic modelling is a complicated task because of its strong variability in time and land terrains. Over a year, wind speed is periodic, showing seasonal variations; however, hourly average wind speed is a stochastic process with a Weibull probability density function; whereas within minutes, it follows a Gaussian distribution [10].Different methods have been employed for time series characterisation of wind processes. Traditionally Weibull distribution is widely used to represent wind speed series at a given site [4,[11][12][13]. Normal, Gamma, Lognormal or combination of these distributions with Weibull distribution [4,7,[14][15][16], empirical wavelet [17] or Kernel density method [18] can also be used to model wind speed series. Shokrzadeh et al. [19] and Kazemi and Goudarzi [20] employed advanced parametric and nonparametric and least square approximation methods to forecast wind power. A typical distribution may not necessarily represent the cumulative wind behaviour of all locations in a region [7]. Thus, the wind speed for a particular location needs to be modelled. The above distributions cannot be used, however, when chronology is considered [4]. A rough observation on the raw wind speed data from Durban demonstrated that the current wind speed depended on the previous wind speed, indicating that chronology should be considered in modelling the wind speeds. Evolutionary algorithms such as genetic algorithm and local search technique are also used in wind speed modelling [21] despite their time consuming procedure [4]. The Markov chain model, which retains chronology and consumes less time, could, therefore, be employed to synthesise wind speed time series for dynamic simulation and wind power f...

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Assessing different parameters estimation methods of Weibull distribution to compute wind power density

Cited by 251 publications

References 59 publications

Comparative Study of Different Methods for Estimating Weibull Parameters: A Case Study on Jeju Island, South Korea

Comparative Study of Different Methods for Estimating Weibull Parameters: A Case Study on Jeju Island, South Korea

Sensor Localization Using Fixed and Dynamic Communication Ranges in Different Types of Distributed Sensor Networks

Characterisation of wind speed series and power in Durban

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