Incorporating air density into a Gaussian process wind turbine power curve model for improving fitting accuracy

Pandit, Ravi; Infield, David; Carroll, J. Douglas

doi:10.1002/we.2285

Cited by 50 publications

(30 citation statements)

References 33 publications

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“…These variations affect the values of the density of the air and therefore the power output. Taking into account this fluctuating parameter makes it possible to improve the power curves and therefore the operating point of the turbines [8]. C p (λ,β) is aerodynamic efficiency and is defined by a nonlinear function of the tip-speed ratio (TSR) λ and blade pitch angle β.…”

Section: Wind Turbine Fundamentalsmentioning

confidence: 99%

Control Strategies for Floating Offshore Wind Turbine: Challenges and Trends

et al. 2019

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The offshore wind resource has huge energy potential. However, wind turbine floating structures have to withstand harsh conditions. Strong wind and wave effects combine to generate vibrations, fatigue, and heavy loads on the structure and other elements of the wind turbine. These structural problems increase maintenance requirements and risk of failure, while reducing availability and energy production. Another challenge for wind energy is to reduce production costs in order to be competitive with other alternatives. From the control point of view, the objective of lowering costs can be achieved by operating the turbine close to its optimum point of operation under partial load, guaranteeing reliability by reducing structural loads and regulating the power generated in strong wind regimes. In this typical and challenging context, this paper proposes a critical state-of-the-art review, discussing challenges and trends on floating offshore wind turbines control.

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Section: Wind Turbine Fundamentalsmentioning

confidence: 99%

Control Strategies for Floating Offshore Wind Turbine: Challenges and Trends

et al. 2019

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“…Machine learning has been used to convert wind speed to power for wind farms where data are available (Mahoney et al, 2012;Parks et al, 2011). Machine learning is best utilized when there is a nonlinear relationship among the predictors and the predictand and the true relationships can be found in the dataset, which is a characteristic of this wind power conversion problem.…”

Section: Machine Learningmentioning

confidence: 99%

“…Bulaevskaya et al, 2015), and the error in converting the wind speed to power. Past research has indicated an advantage in using machine learning methods for wind power conversion (Parks et al, 2011), and we further investigate this in the context of the super-turbine approach. In the superturbine conversion methodology, the wind speed is forecast as a farm-average value, and that wind speed is converted to farm-level power.…”

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confidence: 96%

The super-turbine wind power conversion paradox: using machine learning to reduce errors caused by Jensen's inequality

McCandless

Haupt

2019

Wind Energ. Sci.

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Abstract. Wind power is a variable generation resource and therefore requires accurate forecasts to enable integration into the electric grid. Generally, the wind speed is forecast for a wind plant and the forecasted wind speed is converted to power to provide an estimate of the expected generating capacity of the plant. The average wind speed forecast for the plant is a function of the underlying meteorological phenomena being predicted; however, the wind speed for each turbine at the farm is also a function of the local terrain and the array orientation. Conversion algorithms that assume an average wind speed for the plant, i.e., the super-turbine power conversion, assume that the effects of the local terrain and array orientation are insignificant in producing variability in the wind speeds across the turbines at the farm. Here, we quantify the differences in converting wind speed to power at the turbine level compared with a super-turbine power conversion for a hypothetical wind farm of 100 2 MW turbines as well as from empirical data. The simulations with simulated turbines show a maximum difference of approximately 3 % at 11 m s−1 with a 1 m s−1 standard deviation of wind speeds and 8 % at 11 m s−1 with a 2 m s−1 standard deviation of wind speeds as a consequence of Jensen's inequality. The empirical analysis shows similar results with mean differences between converted wind speed to power and measured power of approximately 68 kW per 2 MW turbine. However, using a random forest machine learning method to convert to power reduces the error in the wind speed to power conversion when given the predictors that quantify the differences due to Jensen's inequality. These significant differences can lead to wind power forecasters overestimating the wind generation when utilizing a super-turbine power conversion for high wind speeds, and indicate that power conversion is more accurately done at the turbine level if no other compensatory mechanism is used to account for Jensen's inequality.

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“…Response: To discuss the other preferred methods of density correction, we added the following from lines 608 to 613: "Note that the air density correction in the IEC 61400-12-1 standard, although often used in practice, assumes the air density remains constant within the 10-minute period (Bulaevskaya et al, 2015). Such assumption oversimplifies real-world meteorological conditions, especially when the observed air density substantially differs from _0 (Pandit et al, 2019). Therefore, Using air density as an independent input in statistical models such as Gaussian process, neural network, and random forest, can lead to smaller power-curve prediction errors than using the air-density-adjusted wind speed (Bulaevskaya et al, 2015;Pandit et al, 2019)."…”

mentioning

confidence: 99%

“…Such assumption oversimplifies real-world meteorological conditions, especially when the observed air density substantially differs from _0 (Pandit et al, 2019). Therefore, Using air density as an independent input in statistical models such as Gaussian process, neural network, and random forest, can lead to smaller power-curve prediction errors than using the air-density-adjusted wind speed (Bulaevskaya et al, 2015;Pandit et al, 2019)." C2…”

mentioning

confidence: 99%