Comparison of modeling methods for wind power prediction: a critical study

doi:10.1007/s11708-018-0553-3

Front. Energy

2020, Vol. 14

Issue (2) : 347-358 https://doi.org/10.1007/s11708-018-0553-3

RESEARCH ARTICLE

Comparison of modeling methods for wind power prediction: a critical study

Rashmi P. SHETTY¹(

), A. SATHYABHAMA¹, P. Srinivasa PAI²

¹. Department of Mechanical Engineering, National Institute of Technology Karnataka, Surathkal 575025, India
². Department of Mechanical Engineering, NMAMIT, Nitte, KarkalaTaluk 574110, India

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Abstract

Prediction of power generation of a wind turbine is crucial, which calls for accurate and reliable models. In this work, six different models have been developed based on wind power equation, concept of power curve, response surface methodology (RSM) and artificial neural network (ANN), and the results have been compared. To develop the models based on the concept of power curve, the manufacturer’s power curve, and to develop RSM as well as ANN models, the data collected from supervisory control and data acquisition (SCADA) of a 1.5 MW turbine have been used. In addition to wind speed, the air density, blade pitch angle, rotor speed and wind direction have been considered as input variables for RSM and ANN models. Proper selection of input variables and capability of ANN to map input-output relationships have resulted in an accurate model for wind power prediction in comparison to other methods.

Keywords power curve method of least squares cubic spline interpolation response surface methodology artificial neural network (ANN)

Corresponding Author(s): Rashmi P. SHETTY

Just Accepted Date: 12 February 2018 Online First Date: 12 April 2018 Issue Date: 22 June 2020

Cite this article:

Rashmi P. SHETTY,A. SATHYABHAMA,P. Srinivasa PAI. Comparison of modeling methods for wind power prediction: a critical study[J]. Front. Energy, 2020, 14(2): 347-358.

URL:

https://academic.hep.com.cn/fie/EN/10.1007/s11708-018-0553-3
https://academic.hep.com.cn/fie/EN/Y2020/V14/I2/347

Fig.1 Methodology adopted in this paper

Tab.1 Power values for various wind speeds from manufacturer’s power curve

Fig.2 Manufacturer’s power curve of the turbine considered for this paper

Tab.2 Sample data set collected from the SCADA

Tab.3 Details of the models developed

Tab.4 Data selected for comparison of performances of the models

Tab.5 Comparison of power predicted by different modeling methods

Fig.3 Surface plot for power by varying two variables: wind speed and pitch angle

Fig.4 Surface plot for power by varying two variables: wind speed and wind direction

Fig.5 Surface plot for power by varying two variables: wind speed and density

Fig.6 Surface plot for power by varying two variables: wind speed and rotor speed

Fig.7 Variation of MSE with number of neurons in the hidden layer for MLP model

Fig.8 Comparison of RMSE of different modeling methods

P	Power output of a wind turbine/kW
r	Air density/(kg·m^–3)
R	Radius of the rotor/m
C_p	Power coefficient
b	Blade pitch angle/(° )
l	Tip speed ratio
v	Wind speed/(m·s^–1)
W_r	Rotor speed/(rad·s^–1)
v_e	Effective wind speed perpendicular to the rotor plane/(m·s^–1)
P_e	Estimated power/kW
$v c$	Cut-in wind speed/(m•s^–1)
$v r$	Rated wind speed/(m•s^–1)
$v f$	Cut-out wind speed/(m·s^–1)
a	Momentum parameter
$η$	Learning rate

Tab.6

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