Monthly and seasonal assessment of wind energy characteristics at four monitored locations in Liguria region (Italy)

the same Weibull parameters. The survey results reveal that applying ELM approach is eventuated in attaining further precision for estimation of both Weibull parameters compared to other methods evaluated. Mean absolute percentage error, mean absolute bias error and root mean square error for k are 8.4600 %, 0.1783 and 0.2371, while for c are 0.2143 %, 0.0118 and 0.0192 m/s, respectively. In conclusion, it is conclusively found that application of ELM is particularly promising as an alternative method to estimate Weibull k and c factors.

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“…Also, Γ(x) is the gamma function which is defined as (Ouammi et al 2010; Mohammadi and Mostafaeipour 2013b):…”

Section: Standard Deviation Methodsmentioning

confidence: 99%

RETRACTED ARTICLE: Application of extreme learning machine for estimation of wind speed distribution

et al. 2015

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“…vi and vf are the cut-in and the cut-out wind speeds, T is the number of hours per year, and ( ) f v is the Weibull function fitted for the specific wind site [7].…”

Section: Available Wind Energy Density Estimationmentioning

confidence: 99%

Toward an Evolutionary Multi-Criteria Model for the Analysis and Estimation of Wind Potential

Amri¹,

Bouattane²,

Khalili³

et al. 2015

JPEE

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The main objective of this paper is to model, analyze and estimate wind energy at East region of Mohammedia and other Moroccan sites. The basic data were taken from meteorological records of each region. In this context, this work is focused on a methodological approach of a decision support system for optimal choice of wind turbine using multi-criteria model that takes into consideration both the accurate Weibull distribution in the area (wind speed-ground roughness) and the technical parameters of the wind turbine. In this approach we realized an adapted modeling of each element of the turbine (rotor-multiplier-generator). This article also offers a way to forecast wind speed in a region where wind data are not accessible using an artificial neural network.

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“…In this paper, two-parameter Weibull distribution is used to simulate the wind speed. Thus, assuming the values of c and k change monthly, the values of c and k from Capo Vado wind farm in [26] are used for further analysis in this paper and the values of c and k are presented in Table 2.…”

Section: Relevant Data Of Wind Farmmentioning

confidence: 99%

A Reliability Model of Wind Farm Considering the Complex Terrain and Cable Failure Based on Clustering Algorithm

Liu¹,

Chen²,

Zhang³

et al. 2014

Journal of Electrical Engineering and Technology

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-A reliability model of wind farm located in mountainous land with complex terrain, which considers the cable and wind turbine (WT) failures, is proposed in this paper. Simple wake effect has been developed to be applied to the wind farm in mountainous land. The component failures in the wind farm like the cable and WT failures which contribute to the wind farm power output (WFPO) and reliability is investigated. Combing the wind speed distribution and the characteristic of wind turbine power output (WTPO), Monte Carlo simulation (MCS) is used to obtain the WFPO. Based on clustering algorithm the multi-state model of a wind farm is proposed. The accuracy of the model is analyzed and then applied to IEEE-RTS 79 for adequacy assessment.

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Monthly and seasonal assessment of wind energy characteristics at four monitored locations in Liguria region (Italy)

Cited by 126 publications

References 21 publications

RETRACTED ARTICLE: Application of extreme learning machine for estimation of wind speed distribution

RETRACTED ARTICLE: Application of extreme learning machine for estimation of wind speed distribution

Toward an Evolutionary Multi-Criteria Model for the Analysis and Estimation of Wind Potential

A Reliability Model of Wind Farm Considering the Complex Terrain and Cable Failure Based on Clustering Algorithm

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