Review of power curve modelling for wind turbines

Carrillo, C.; Montaño, A.F. Obando; Cidrás, J.; Diaz-Dorado, E.

doi:10.1016/j.rser.2013.01.012

Cited by 304 publications

(181 citation statements)

References 17 publications

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“…The comparisons were performed at two power rating range, one for 750-900 kW and the one for 1500-1700 kW. The rotors specifications and the power curves of conventional turbine that used for the comparison are listed in Table II [19,20]. Figures 9-10 illustrate the results of the comparisons An important factor that determines the energy production is probability wind speed over a long period.…”

Section: Annual Engery Productionmentioning

confidence: 99%

Reconfiguration of a wind turbine with hydrostatic drivetrain to improve annual energy production

Deldar

Izadian

Anwar

2015

2015 IEEE Energy Conversion Congress and Exposition (ECCE)

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Abstract-Hydrostatic transmission systems (HTS) have shown potential in replacing gearbox in conventional wind turbines. However, the general perception about these systems is that they suffer from low efficiencies, specifically at low wind speeds. This paper presents a novel technique that can improve the annual energy production (AEP) beyond that of a conventional wind turbine. By optimizing the operating conditions and the design of the wind turbine, the performance and efficiency of a HTS can be improved. A side-by-side comparison with the conventional wind turbines is provided to highlight the benefits of the proposed methodology. One of the findings of this research is that, rotor, hydrostatic pump, motor and their operations' planning must be optimized together to achieve higher AEP. The reconfigured turbines are shown to provide up to 8 percent AEP increase for a 750 kW plant and up to 10 percent increase for 1500 kW plants using the proposed drivetrain configurations.

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Section: Annual Engery Productionmentioning

confidence: 99%

Reconfiguration of a wind turbine with hydrostatic drivetrain to improve annual energy production

Deldar

Izadian

Anwar

2015

2015 IEEE Energy Conversion Congress and Exposition (ECCE)

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“…Carrillo et al [33] tested various approximations for developing continuous power curves and found that the cubic and exponential approximations provide the best fit in terms of energy density. Therefore for the current analysis, the cubic power curve approximation was deemed sufficient.…”

Section: Generator Constraintsmentioning

confidence: 99%

Current and near-term GHG emissions factors from electricity production for New York State and New York City

2017

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Citation: HOWARD Abstract This paper reports estimates of average GHG emissions factors for New York State and marginal GHG emissions factors for interventions in New York City. A multi-regional unit commitment model was developed to simulate the behavior of the grid. The parameters defining the system operation were gathered from several publicly available data sources including historical hourly electricity production and fuel consumption from over one hundred power plants. Factors were estimated for a baseline year of 2011 and subsequently for the year 2025 considering planned power plant additions and retirements. Future scenarios are also developed considering different wind turbine installation growth rates and policies affecting the cost of generation from coal power plants. The work finds marginal GHG emissions factors for New York City could reduce between 30 and 36% from 540 kg CO 2 e/MWh in 2011 for all future scenarios considered. Average GHG emissions factors for New York State could reduce 9 to 39% from 215 kg CO 2 e/MWh depending on the wind growth rate and price burden on coal power plants.

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“…Power curve values are developed from field measurements and can be used for studies involving energy calculations [47]. There are three important points characterizing a typical power curve ( Figure 2): (1) at the cut-in speed the wind turbine starts to generate usable power; (2) after exceeding the rated output speed the maximum output power (rated power) is generated; and (3) after exceeding the cut-out speed turbines cease power generation and shut down [46].…”

Section: Annual Wind Energy Yield Estimationmentioning

confidence: 99%

High Spatial Resolution Simulation of Annual Wind Energy Yield Using Near-Surface Wind Speed Time Series

Jung

2016

Energies

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Abstract:In this paper a methodology is presented that can be used to model the annual wind energy yield (AEY mod ) on a high spatial resolution (50 mˆ50 m) grid based on long-term (1979-2010) near-surface wind speed (U S ) time series measured at 58 stations of the German Weather Service (DWD). The study area for which AEY mod is quantified is the German federal state of Baden-Wuerttemberg. Comparability of the wind speed time series was ensured by gap filling, homogenization and detrending. The U S values were extrapolated to the height 100 m (U 100m,emp ) above ground level (AGL) by the Hellman power law. All U 100m,emp time series were then converted to empirical cumulative distribution functions (CDF emp ). 67 theoretical cumulative distribution functions (CDF) were fitted to all CDF emp and their goodness of fit (GoF) was evaluated. It turned out that the five-parameter Wakeby distribution (WK5) is universally applicable in the study area. Prior to the least squares boosting (LSBoost)-based modeling of WK5 parameters, 92 predictor variables were obtained from: (i) a digital terrain model (DTM), (ii) the European Centre for Medium-Range Weather Forecasts re-analysis (ERA)-Interim reanalysis wind speed data available at the 850 hPa pressure level (U 850hPa ), and (iii) the Coordination of Information on the Environment (CORINE) Land Cover (CLC) data. On the basis of predictor importance (PI) and the evaluation of model accuracy, the combination of predictor variables that provides the best discrimination between U 100m,emp and the modeled wind speed at 100 m AGL (U 100m,mod ), was identified. Results from relative PI-evaluation demonstrate that the most important predictor variables are relative elevation (Φ) and topographic exposure (τ) in the main wind direction. Since all WK5 parameters are available, any manufacturer power curve can easily be applied to quantify AEY mod .

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Review of power curve modelling for wind turbines

Cited by 304 publications

References 17 publications

Reconfiguration of a wind turbine with hydrostatic drivetrain to improve annual energy production

Reconfiguration of a wind turbine with hydrostatic drivetrain to improve annual energy production

Current and near-term GHG emissions factors from electricity production for New York State and New York City

High Spatial Resolution Simulation of Annual Wind Energy Yield Using Near-Surface Wind Speed Time Series

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