Normalized power curves as a tool for identification of optimum wind turbine generator parameters

Jangamshetti, Suresh H.; Rau, V. G.

doi:10.1109/60.937209

Cited by 104 publications

(48 citation statements)

References 2 publications

Supporting

Mentioning

Contrasting

Unclassified

Order By: Relevance

“…Hence in the wind power equation P = power output of the turbine under consideration should be a minimum of 1600 kW and 3.036u 3 for a given wind speed.…”

Section: Wind Power Modelling and Optimizationmentioning

confidence: 99%

“…The key areas of wind energy in which reduction of cost can be achieved include, site selection, layout design, predictive maintenance etc. [3][4][5][6]. On the other hand, maximizing the power output of a wind turbine for a given wind speed between a cut in and cut out wind speeds through effective control strategies can also fetch benefits [7,8].…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Wind Power Optimization: A Comparison of Meta-Heuristic Algorithms

Shetty

Sathyabhama

Pai³

2018

IOP Conf. Ser.: Mater. Sci. Eng.

View full text Add to dashboard Cite

“…Hence in the wind power equation P = power output of the turbine under consideration should be a minimum of 1600 kW and 3.036u 3 for a given wind speed.…”

Section: Wind Power Modelling and Optimizationmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Wind Power Optimization: A Comparison of Meta-Heuristic Algorithms

Shetty

Sathyabhama

Pai³

2018

IOP Conf. Ser.: Mater. Sci. Eng.

View full text Add to dashboard Cite

“…Where: ρ, air density S, turbine cross sectional area Db, blade diameter Cp, power coefficient λ, tip ratio , rated velocity (CF), capacity factor According reference [8] (CF) is determined by:…”

Section: A Some Considerations About Wind Energymentioning

confidence: 99%

Flux switching alternators for small wind generation

Andrada¹,

Garcia²

2016

REPQJ

View full text Add to dashboard Cite

Abstract. This paper considers the application of flux switching alternators for small wind generation. First, after a brief presentation of the small wind energy systems, a description and the fundamentals of flux switching machines is given, then the state of art of flux switching generators is presented. Finally a critical assessment is shown, considering the main advantages and drawbacks of this type of machines as alternator for use in small wind generation systems. . Key wordsSmall wind power generation, alternators for wind energy, flux switching machines.

show abstract

“…Wind speed probability modeling and estimation of wind turbine capacity factor for a site are investigated by many researchers. Jangamshetti & Rau (1999, 2001) used normalized power curves as a tool for identification of optimum wind turbine generator parameters. Rehman and Ahmad (2004) analyzed wind data for five coastal locations.…”

Section: Introductionmentioning

confidence: 99%

Comparison of Weibull parameters computation methods and analytical estimation of wind turbine capacity factor using polynomial power curve model: case study of a wind farm

Saxena

Rao

2015

Renewables

View full text Add to dashboard Cite

Introduction: Wind speed probability at a site has to be modeled for evaluating the energy generation potential of a wind farm. Analytical computation of wind turbine capacity factor at the planning stage of a wind farm is very crucial. Thus, the comparison of Weibull parameters estimation methods and computation of wind turbine capacity factor are the focus of this paper. Case description: Soda wind farm used in this case study is located in the Jaisalmer district of western Rajasthan in India. Modeling of wind speed probability and power curve of wind turbines installed at Soda site were done for analytically estimating the capacity factor of wind turbine. Estimated capacity factors were then compared with the measured values of wind farm for validation of results. Discussion and evaluation: Four numerical methods namely graphical, empirical, modified maximum likelihood, and energy pattern factor were used for month-wise Weibull parameters estimation at hub height of 65 m. Power curve of the wind turbine installed at site was modeled using eighth-degree polynomial. Coefficients of polynomial were calculated from the combined use of linear least square method and QR decomposition using Gram-Schmidt orthogonalization method. Conclusions: Results show that the percentage error in annual capacity factor estimation using Weibull parameters estimated from graphical, empirical, modified maximum likelihood, and energy pattern factor methods were +9.98%, −1.59%, −1.22%, and −1.29%, respectively. Annual capacity factor that was estimated using the Weibull parameters calculated from modified maximum likelihood method matched best with the measured values. Graphical method gave the most erroneous results.

show abstract

Normalized power curves as a tool for identification of optimum wind turbine generator parameters

Cited by 104 publications

References 2 publications

Wind Power Optimization: A Comparison of Meta-Heuristic Algorithms

Wind Power Optimization: A Comparison of Meta-Heuristic Algorithms

Flux switching alternators for small wind generation

Comparison of Weibull parameters computation methods and analytical estimation of wind turbine capacity factor using polynomial power curve model: case study of a wind farm

Contact Info

Product

Resources

About