Non-linear regression model for wind turbine power curve

Marčiukaitis, Mantas; Žutautaitė, Inga; Martišauskas, Linas; Jokšas, Benas; Gecevičius, Giedrius; Sfetsos, Athanasios

doi:10.1016/j.renene.2017.06.039

Cited by 99 publications

(53 citation statements)

References 20 publications

Supporting

Mentioning

Contrasting

Unclassified

Order By: Relevance

“…The results were evaluated and discussed. Ciukaitis et al [35] proposed a non-linear regression model for the power curve. Asghar [36,37] presented a new control algorithm based on adaptive neuro-fuzzy inference system (ANFIS) for turbine power coefficient as a function of tip-speed ratio and pitch angle.…”

Section: Introductionmentioning

confidence: 99%

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

et al. 2019

View full text Add to dashboard Cite

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.

show abstract

Section: Introductionmentioning

confidence: 99%

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

et al. 2019

View full text Add to dashboard Cite

show abstract

“…Lately, the technological significance of electrical power production by employing wind turbines has increased and will keep doing it over the next decades [1]- [4], [8]. Moreover, the total installed capacity of wind turbines has also notably growth, for instance, in China, America, and Europe [4], [5].…”

Section: Introductionmentioning

confidence: 99%

A Wind Turbine Blade Damage Detection System Based on Data Analysis: An Academic Example on the Use of Histograms

Acho¹,

Buenestado²

2018

REPQJ

View full text Add to dashboard Cite

The main objective of this paper is to introduce an academic example on the use of histograms to develop a wind turbine blade fault (or damage) detection system. This approach merely uses data analysis obtained from a generated wind turbine power. Then, and based on the power curve of a wind turbine, histograms are obtained, first, for the healthy wind turbine operation, and then, for two cases on faulty wind turbine operations. By comparing them, a faulty behaviour of a wind turbine is detectable. Finally, according to our numerical example, the proposed technique seems a realistic case to students on the statistic and probability fields as a useful academic example.

show abstract

“…To this end, an accurate forecast is critical as well as knowing the uncertainty associated with that prediction [3].…”

Section: Introductionmentioning

confidence: 99%

“…Wind turbines seldom operate under such conditions [5], and there is often a discrepancy between the empirical and theoretical power curve, which results in inaccurate power prediction and motivates some research works in the recent years [3,6,7].…”

Section: Introductionmentioning

confidence: 99%

“…The parametric models are built from a set of mathematical equations that include some parameters that have to be determined through regression methods from the available data for weather conditions and power generation [3]. Through the years many parametric models have been proposed, examples include: the piecewise linear model [8,9], polynomial power curve of various degrees as discussed in [10,11], the maximum principle method by Rauh as proposed in [12], the dynamical approach using the Langevin Model presented in [12], a probabilistic method proposed in [13], the so-called ideal power curve [14], four and five parameters logistic functions [5,15] , as well as Modified hyperbolic tangent (MHTan) function [6].…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

A Simple and Effective Approach for the Prediction of Turbine Power Production From Wind Speed Forecast

Marrocu

Massidda

2017

Energies

View full text Add to dashboard Cite

An accurate forecast of the power generated by a wind turbine is of paramount importance for its optimal exploitation. Several forecasting methods have been proposed either based on a physical modeling or using a statistical approach. All of them rely on the availability of high quality measures of local wind speed, corresponding generated power and on numerical weather forecasts. In this paper, a simple and effective wind power forecast technique, based on the probability distribution mapping of wind speed forecast and observed power data, is presented and it is applied to two turbines located on the island of Borkum (Germany) in the North Sea. The wind speed forecast of the ECMWF model at 100 m from the ground is used as the prognostic meteorological parameter. Training procedures are based entirely on relatively short time series of power measurements. Results show that our approach has skills that are similar or better than those obtained using more standard methods when measured with mean absolute error.

show abstract

Non-linear regression model for wind turbine power curve

Cited by 99 publications

References 20 publications

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

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

A Wind Turbine Blade Damage Detection System Based on Data Analysis: An Academic Example on the Use of Histograms

A Simple and Effective Approach for the Prediction of Turbine Power Production From Wind Speed Forecast

Contact Info

Product

Resources

About