The ideal power curve of small wind turbines from field data

Trivellato, F.; Battisti, Lorenzo; Miori, Giordano

doi:10.1016/j.jweia.2012.04.026

Cited by 37 publications

(12 citation statements)

References 9 publications

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“…LSWTs control techniques are focused on three main criteria: (i) power optimisation [27][28][29], (ii) blade pitch angle adjustment for the varying wind speeds and wind directions [30][31][32] and (iii) generator torque to stabilise technical parts of the WT [29,[33][34][35]. Recent trends to encompass the components of mechanical, aerodynamic, social and economic fields make the control techniques multi input and multi output (MIMO) [36].…”

Section: Wt Control and Developmentsmentioning

confidence: 99%

Synergies of Wind Turbine control techniques

Bertašienė

Azzopardi

2015

Renewable and Sustainable Energy Reviews

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Section: Wt Control and Developmentsmentioning

confidence: 99%

Synergies of Wind Turbine control techniques

Bertašienė

Azzopardi

2015

Renewable and Sustainable Energy Reviews

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“…Realistic sizing of wind energy integration can be obtained from the wind turbine power characteristics curve [3,4]. Forecasting of wind power and the planning of wind farm expansion are able to the accurate computation of power characteristics curves [5,6]. The turbine performance specification and an indication of wind turbine service life can be deduced from the power characteristics curve.…”

Section: Introductionmentioning

confidence: 99%

The Prediction Model of Characteristics for Wind Turbines Based on Meteorological Properties Using Neural Network Swarm Intelligence

et al. 2019

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In order to produce more efficient, sustainable-clean energy, accurate prediction of wind turbine design parameters provide to work the system efficiency at the maximum level. For this purpose, this paper appears with the aim of obtaining the optimum prediction of the turbine parameter efficiently. Firstly, the motivation to achieve an accurate wind turbine design is presented with the analysis of three different models based on artificial neural networks comparatively given for maximum energy production. It is followed by the implementation of wind turbine model and hybrid models developed by using both neural network and optimization models. In this study, the ANN-FA hybrid structure model is firstly used and also ANN coefficients are trained by FA to give a new approach in literature for wind turbine parameters’ estimation. The main contribution of this paper is that seven important wind turbine parameters are predicted. Aiming to fill the mentioned research gap, this paper outlines combined forecasting turbine design approaches and presents wind turbine performance in detail. Furthermore, the present study also points out the possible further research directions of combined techniques so as to help researchers in the field develop more effective wind turbine design according to geographical conditions.

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“…The "method of bins" is a data reduction technique used to normalize the data to construct the measured power curve. This binned power curve includes the effect of the site turbulence and all other effects reflecting onsite conditions [12].…”

Section: Introductionmentioning

confidence: 99%

Comparative assessments of binned and support vector regression-based blade pitch curve of a wind turbine for the purpose of condition monitoring

Pandit

Infield

2018

Int J Energy Environ Eng

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The unexpected failure of wind turbine components leads to significant downtime and loss of revenue. To prevent this, supervisory control and data acquisition (SCADA) based condition monitoring is considered as a cost-effective approach. In several studies, the wind turbine power curve has been used as a critical indicator for power performance assessment. In contrast, the application of the blade pitch angle curve has hardly been explored for wind turbine condition monitoring purposes. The blade pitch angle curve describes the nonlinear relationship between pitch angle and hub height wind speed and can be used for the detection of faults. A support vector machine (SVM) is an improved version of an artificial neural networks (ANN) and is widely used for classification-and regression-related problems. Support vector regression is a data-driven approach based on statistical learning theory and a structural risk minimization principle which provides useful nonlinear system modeling. In this paper, a support vector regression (a nonparametric machine learning approach)-based pitch curve is presented and its application to anomaly detection explored for wind turbine condition monitoring. A radial basis function (RBF) was used as the kernel function for effective SVR blade pitch curve modeling. This approach is then compared with a binned pitch curve in the identification of operational anomalies. The paper will outline the advantages and limitations of these techniques.

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The ideal power curve of small wind turbines from field data

Cited by 37 publications

References 9 publications

Synergies of Wind Turbine control techniques

Synergies of Wind Turbine control techniques

The Prediction Model of Characteristics for Wind Turbines Based on Meteorological Properties Using Neural Network Swarm Intelligence

Comparative assessments of binned and support vector regression-based blade pitch curve of a wind turbine for the purpose of condition monitoring

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