Wind turbine selection method based on the statistical analysis of nominal specifications for estimating the cost of energy

Arias-Rosales, Andrés; Osorio-Gómez, Gilberto

doi:10.1016/j.apenergy.2018.06.103

Cited by 26 publications

(9 citation statements)

References 51 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Regression analysis has been applied in the energy field, eg, by Lee and Yang, Fumo and Rafe Biswas, and Ma et al In the wind energy field, Arias‐Rosales and Osorio‐Gómez have applied regression analysis to wind turbines based on estimates of the cost of energy.…”

Section: Resultsmentioning

confidence: 99%

Technology effects in repowering wind turbines

2019

View full text Add to dashboard Cite

This research investigates, analyses, and quantifies the technological effects of wind turbine repowering (ie, where old turbines are removed and new turbines are installed at the same or a very close location, including the enhanced performance in energy production). In these cases, it is assumed that both old and new turbines are subject to the same wind regime, other than because of technological elements, such as hub height, and thus it is possible to isolate the effects of new technology from the effect of changing local wind conditions. This research is based on the analysis of empirical data on repowering turbines in Denmark and Germany, and on historical production data available for the Danish component of the data set. Technological innovations are expected to enable new wind turbines to capture more energy at the repowering site, mostly through larger rotors and higher hub heights, and this is what this study has analysed. The results show that new turbines in repowering projects are twice as high, have three times the rotor diameter, nine times the swept area, six times the nominal power, and nine times as much electricity as the old turbines. However, the most significant improvement is probably the increase of capacity factor of 7.1% on a per-turbine basis, or 9.7% on a per-production basis.

show abstract

Section: Resultsmentioning

confidence: 99%

Technology effects in repowering wind turbines

2019

View full text Add to dashboard Cite

show abstract

“…As mentioned in section 1, the typical model of the wind turbine consists of two main performance regions. The output power of the wind turbine in both performance regions is given by [8,[15][16][17]:…”

Section: Derivation Of Energy Generated By the Wind Turbinementioning

confidence: 99%

Comparative study between measured and estimated wind energy yield

Al-Quraan¹,

Al-Mahmodi²,

Radaideh³

et al. 2020

Turk J Elec Eng & Comp Sci

View full text Add to dashboard Cite

This paper proposes a power-speed (P-V) model of the wind turbine by assuming three different functions for the first performance region; cubic, quadratic and uncorrected cubic. These three functions have been compared with the manufacturer models of five different wind turbines which were installed in five different locations in Jordan; Tafila, Hofa, Fujeij, Al Rajef, and Deahan. The wind turbine of these wind farms are considered as large scale HAWT in the range of Mw. The generated P-V models are developed by applying a new method described in this paper which is basically based on generating a multiplier factor x. In this study, the quadratic model shows the highest correlation compared with the other models. The wind energy yield for the selected wind farms has been estimated by a mathematical modelling based on Rayleigh distribution function, derived in this paper. The energy yield using this mathematical model has been compared with the measured energy output of four wind farms, Tafila, Hofa, Al Rajef, and Deahan. The measured energy were provided by the operators of these wind farms which are: Jordan Wind Project Company(JWPC), Central Electricity Generation Company (CEGC), Green Watts Renewable Energy (GWRC)and Korean Southern Power Company(KOSPO). Results show that the estimated energy using the quadratic wind turbine model for all wind farms are very close to the actual output. Accuracy analysis for the quadratic model resulted in an error of less than 10 % between the measured and estimated energy output for all wind farms. The capacity factors for the selected wind farms have been estimated using the quadratic P-V model. Results show that Tafila wind farm has the highest capacity factor which is around 47 % .

show abstract

“…Ali et al 6 applied the Weibull distribution and Rayleigh distribution for the wind speed data. Arias-Rosales and Osorio-Gómez 7 used the statistical analysis for estimating energy cost. The comparisons of wind speed distributions can be seen in 8 – 11 used the Weibull distribution for modeling wind speed data.…”

Section: Introductionmentioning

confidence: 99%

Testing average wind speed using sampling plan for Weibull distribution under indeterminacy

Aslam

2021

Sci Rep

View full text Add to dashboard Cite

The time truncated plan for the Weibull distribution under the indeterminacy is presented. The plan parameters of the proposed plan are determined by fixing the indeterminacy parameter. The plan parameters are given for various values of indeterminacy parameters. From the results, it can be concluded that the values of sample size reduce as indeterminacy values increase. The application of the proposed plan is given using wind speed data. From the wind speed example, it is concluded that the proposed plan is helpful to test the average wind speed at smaller values of sample size as compared to existing sampling plan.

show abstract

Wind turbine selection method based on the statistical analysis of nominal specifications for estimating the cost of energy

Cited by 26 publications

References 51 publications

Technology effects in repowering wind turbines

Technology effects in repowering wind turbines

Comparative study between measured and estimated wind energy yield

Testing average wind speed using sampling plan for Weibull distribution under indeterminacy

Contact Info

Product

Resources

About