Estimation of Energy Yield From Wind Farms Using Artificial Neural Networks

Mabel, M. Carolin; Fernandez, Eugene

doi:10.1109/tec.2008.2001458

Cited by 63 publications

(21 citation statements)

References 14 publications

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“…The chaotic behaviour is what makes the long-term predictions of wind speed erroneous, but it should be possible to obtain better short-term predictions using the deterministic model than would otherwise be made with the statistical methods. It is reported that short-term predictions of one to six hours ahead at intervals of 10 min are important in power dispatching systems (Mabel and Fernandez, 2009).…”

Section: R C Sreelekshmi Et Al: Deterministic Nature Of Surface Wimentioning

confidence: 99%

“…Presently, there is extensive literature on various areas related to wind energy acquisition and utilisation such as wind speed modelling and prediction, wind power production and wind resource quantification (e.g. Finzi et al, 1984;Martín et al, 1999;Elliott, 2004;Celik, 2004;von Bremen, 2007;Mabel and Fernandez, 2009;Kavasseri and Seetharaman, 2009;Bantaa et al, 2011).…”

Section: Introductionmentioning

confidence: 99%

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Deterministic nature of the underlying dynamics of surface wind fluctuations

Sreelekshmi

Asokan²,

Kumar

2012

Ann. Geophys.

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Abstract. Modelling the fluctuations of the Earth's surface wind has a significant role in understanding the dynamics of atmosphere besides its impact on various fields ranging from agriculture to structural engineering. Most of the studies on the modelling and prediction of wind speed and power reported in the literature are based on statistical methods or the probabilistic distribution of the wind speed data. In this paper we investigate the suitability of a deterministic model to represent the wind speed fluctuations by employing tools of nonlinear dynamics. We have carried out a detailed nonlinear time series analysis of the daily mean wind speed data measured at Thiruvananthapuram (8.483 • N,76.950 • E) from 2000 to 2010. The results of the analysis strongly suggest that the underlying dynamics is deterministic, low-dimensional and chaotic suggesting the possibility of accurate short-term prediction. As most of the chaotic systems are confined to laboratories, this is another example of a naturally occurring time series showing chaotic behaviour.

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Section: R C Sreelekshmi Et Al: Deterministic Nature Of Surface Wimentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Deterministic nature of the underlying dynamics of surface wind fluctuations

Sreelekshmi

Asokan²,

Kumar

2012

Ann. Geophys.

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show abstract

“…where α refers to the Albert Betz constant, ρ indicates the air density in kilograms per cubic meters, A represents the swept area of wind turbine in square meters, and V is the wind speed in meters per second [71].…”

Section: Wind Energymentioning

confidence: 99%

A review on electric vehicles interacting with renewable energy in smart grid

Liu

Kong

Liu

et al. 2015

Renewable and Sustainable Energy Reviews

260

107

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“…Unpredictability and variability of wind power generation is one of the fundamental difficulties faced by power system operators. Good forecasting and modelling tools are urgently needed for the efficient integration of wind energy into the power system (Lei et al, 2009;Soleimanzadeh et al, 2012;Wen et al, 2009), especially for forecasting of the power generating by wind farms (Callaway, 2010;Kusiak et al, 2009b;Kusiak and Zhang, 2010;Mabel and Fernandez, 2009). Furthermore, condition monitoring of wind turbines, including fault diagnostics, in particular at the early stage of a fault occurrence or even participatory actions, is an essential problem in wind turbines engineering in particular (Hameeda et al, 2009;Jab"oń ski et al, 2011;Kusiak and Li, 2011;Kusiak et al, 2009a;Watson et al, 2010) and in rotating machinery engineering in general .…”

Section: Introductionmentioning

confidence: 99%