Journal ArticleDOI
Review of power curve modelling for wind turbines
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In this article, a review of the equations commonly used to represent the power curves of variable speed wind turbine generators (VSWTs) is carried out, which shows that the exponential and cubic approximations give the higher R 2 values and the lower error in energy estimation.Abstract:
Currently, variable speed wind turbine generators (VSWTs) are the type of wind turbines most widely installed. For wind energy studies, they are usually modelled by means the approximation of the manufacturer power curve using a generic equation. In literature, several expressions to do this approximation can be found; nevertheless, there is not much information about which is the most appropriate to represent the energy produced by a VSWT. For this reason, in this paper, it is carried out a review of the equations commonly used to represent the power curves of VSWTs: polynomial power curve, exponential power curve, cubic power curve and approximate cubic power curve. They have been compared to manufacturer power curves by using the coefficients of determination, as fitness indicators, and by using the estimation of energy production. Data gathered from nearly 200 commercial VSWTs, ranging from 225 to 7500 kW, has been used for this analysis. Results of the analysis presented in the paper show that exponential and cubic approximations give the higher R 2 values and the lower error in energy estimation. With the approximate cubic power curve quite high values of R 2 and low errors in energy estimation are achieved, which makes this kind of approximation very interesting due to its simplicity. Finally, the polynomial power curve shows the worst results mainly due to its sensitivity to the data given by the manufacturer.read more
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Journal ArticleDOI
Probabilistic modelling of wind turbine power curves with application of heteroscedastic Gaussian Process regression
Timothy J. Rogers,Paul Gardner,Nikolaos Dervilis,Keith Worden,A.E. Maguire,Evangelos Papatheou,Elizabeth J. Cross +6 more
TL;DR: This work proposes the use of a heteroscedastic Gaussian Process model, which exists within a Bayesian framework which exhibits built-in protection against over-fitting and robustness to noisy measurements, and is shown to be effective on data collected from an operational wind turbine.
Journal ArticleDOI
Western Iberian offshore wind resources: More or less in a global warming climate?
Pedro M. M. Soares,Daniela C. A. Lima,Rita M. Cardoso,Manuel L. Nascimento,Alvaro Semedo,Alvaro Semedo +5 more
TL;DR: In this paper, the authors analyse the Western Iberian offshore wind resource for present and future climates, using a set of regional climate models (RCMs) simulations produced in the framework of the CORDEX experiment at 0.11° resolution (∼12") and a regional climate simulation produced with the WRF model at higher resolution (9 km).
Journal ArticleDOI
Combined Floating Offshore Wind and Solar PV
TL;DR: In this paper, the authors explored the potential of combining offshore wind and solar power through a case study in Asturias (Spain), a region where floating solutions are the only option for marine renewables due to the lack of shallow water areas, which renders bottom-fixed wind turbines inviable.
Journal ArticleDOI
Modelling and analysis of real-world wind turbine power curves: Assessing deviations from nominal curve by neural networks
TL;DR: A model based on the use of an Artificial Neural Networks that can predict the amount of delivered power can be used for any wind turbine to verify real performances and to check fault conditions helping operators in understanding normal and abnormal behaviour.
Journal ArticleDOI
Realistic Wind Farm Layout Optimization through Genetic Algorithms Using a Gaussian Wake Model
Abstract: Wind Farm Layout Optimization (WFLO) can be useful to minimize power losses associated with turbine wakes in wind farms. This work presents a new evolutionary WFLO methodology integrated with a recently developed and successfully validated Gaussian wake model (Bastankhah and Porte-Agel model). Two different parametrizations of the evolutionary methodology are implemented, depending on if a baseline layout is considered or not. The proposed scheme is applied to two real wind farms, Horns Rev I (Denmark) and Princess Amalia (the Netherlands), and two different turbine models, V80-2MW and NREL-5MW. For comparison purposes, these four study cases are also optimized under the traditionally used top-hat wake model (Jensen model). A systematic overestimation of the wake losses by the Jensen model is confirmed herein. This allows it to attain bigger power output increases with respect to the baseline layouts (between 0.72% and 1.91%) compared to the solutions attained through the more realistic Gaussian model (0.24–0.95%). The proposed methodology is shown to outperform other recently developed layout optimization methods. Moreover, the electricity cable length needed to interconnect the turbines decreases up to 28.6% compared to the baseline layouts.
References
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