Wind energy analysis based on turbine and developed site power curves: A case-study of Darling City

Olaofe, Zaccheus O.; Folly, Komla A.

doi:10.1016/j.renene.2012.11.003

Cited by 43 publications

(29 citation statements)

References 7 publications

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“…This distribution is most widely accepted for the study of wind data. It can be expressed as the formula stated in equation (5) [22,23];…”

Section: Weibull Distribution Functionmentioning

confidence: 99%

Statistical Analysis of Wind Speed and Evaluation of Wind Power Density for Colaba, Mumbai

Francis¹,

Nalamutt²

2019

IJARET

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The fuel energy resources around the globe are declining at a faster rate. The greenhouse gases emitted in the process of harnessing energy from the non-renewable resources such as coal, fossil-fuel and radioactive substances cause the global warming and there is a danger of climate change. All these factors compel the researchers to explore renewable sources for power generation. Wind is a renewable resource, which is available naturally and is practically inexhaustible. In this study, the hourly wind data collected between the years 2013 to 2015 is evaluated. The wind data at the height of 11 m was obtained from a wind station at the Indian Meteorological Department, Colaba (Mumbai, India). The highest monthly wind speed measured was 5.11 m/s and the yearly average was found to be 2.72 m/s. various distribution functions were statistically analyzed and it was observed that the Two-parameter Weibull distribution gives the best fit to the actual data. The mean wind power density was 73.013 W/m2. The maximum wind speeds can be observed between the months of June and September. The wind data analysis shows that if a domestic wind turbine is installed at this site, it will generate enough energy for power saving. This paper aims to promote the growth of small wind power plant in a developing country like India.

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“…This distribution is most widely accepted for the study of wind data. It can be expressed as the formula stated in equation (5) [22,23];…”

Section: Weibull Distribution Functionmentioning

confidence: 99%

Statistical Analysis of Wind Speed and Evaluation of Wind Power Density for Colaba, Mumbai

Francis¹,

Nalamutt²

2019

IJARET

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“…The pressure, temperature, and humidity of site affect the air density [17], hence affecting the power produced. Effect of varying air density has been considered for developing site specific curves [18]. It is shown in [2] that temperature has the highest influence on air density and considering its effect along with the wind direction resulted in improved performance of models.…”

Section: Influencing Factorsmentioning

confidence: 99%

A Critical Review on Wind Turbine Power Curve Modelling Techniques and Their Applications in Wind Based Energy Systems

2016

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Power curve of a wind turbine depicts the relationship between output power and hub height wind speed and is an important characteristic of the turbine. Power curve aids in energy assessment, warranty formulations, and performance monitoring of the turbines. With the growth of wind industry, turbines are being installed in diverse climatic conditions, onshore and offshore, and in complex terrains causing significant departure of these curves from the warranted values. Accurate models of power curves can play an important role in improving the performance of wind energy based systems. This paper presents a detailed review of different approaches for modelling of the wind turbine power curve. The methodology of modelling depends upon the purpose of modelling, availability of data, and the desired accuracy. The objectives of modelling, various issues involved therein, and the standard procedure for power performance measurement with its limitations have therefore been discussed here. Modelling methods described here use data from manufacturers’ specifications and actual data from the wind farms. Classification of modelling methods, various modelling techniques available in the literature, model evaluation criteria, and application of soft computing methods for modelling are then reviewed in detail. The drawbacks of the existing methods and future scope of research are also identified.

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“…A visual comparison of the fitted distribution with the histogram of sample wind speed is a commonly used and convenient method [23,32]. A number of statistical indicators to measure the goodness of fit have been used in various works, which include RMSE, R 2 , chi square, Kolmogorov-Smirnov, and Anderson-Darling tests [3,6,18].…”

Section: Model Evaluationmentioning

confidence: 99%

“…Wake effect of the turbines is neglected. The turbines in a wind farm may produce less power due to the wake effect and underperformance of some turbines; therefore, instead of the manufacturer's curve, using the power curve derived from the actual data of wind farm can give better results in energy estimation [23]. Hence, in addition to the manufacturer's curve, power curves derived from the actual data of wind farm site A are also used for energy analyses.…”

Section: Introductionmentioning

confidence: 99%

A comparative analysis of wind speed probability distributions for wind power assessment of four sites

Sohoni¹,

Gupta²,

Nema³

2016

Turk J Elec Eng & Comp Sci

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Abstract:In this paper, five probability distribution functions are employed to fit the wind speed data from four different geographical locations in the world in a preliminary analysis. These wind regimes are selected such that they represent wide ranges of mean wind speeds and present different shapes of wind speed histograms. The wind speed data used for modelling consist of 10-min average SCADA data from three US wind farms and hourly averages recorded at a weather station in Canada. Out of the five, three functions, namely Weibull, Rayleigh, and gamma, which provide a better fit to the data, are selected to carry out further analyses. This study investigates the ability of these functions to match different statistical descriptions of wind regimes. Parameter estimation is done by the method of moments, and models are evaluated by root mean square error and R square methods. The suitability of PDFs to predict the wind power densities and annual energy production using manufacturers' power curve data at three of the selected sites is analysed.Power curves extracted from actual data of one wind farm using novel four-and five-parameter logistic approximations are also introduced here for energy analyses.

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Wind energy analysis based on turbine and developed site power curves: A case-study of Darling City

Cited by 43 publications

References 7 publications

Statistical Analysis of Wind Speed and Evaluation of Wind Power Density for Colaba, Mumbai

Statistical Analysis of Wind Speed and Evaluation of Wind Power Density for Colaba, Mumbai

A Critical Review on Wind Turbine Power Curve Modelling Techniques and Their Applications in Wind Based Energy Systems

A comparative analysis of wind speed probability distributions for wind power assessment of four sites

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