TY  - EJOU
AU  - Tai, Vin Cent 
AU  - Tan, Yong Chai 
AU  - Rahman, Nor Faiza Abd 
AU  - Chia, Chee Ming 
AU  - Zhakiya, Mirzhakyp 
AU  - Saw, Lip Huat 

TI  - A Novel Power Curve Prediction Method for Horizontal-Axis Wind Turbines Using Artificial Neural Networks
T2  - Energy Engineering

PY  - 2021
VL  - 118
IS  - 3
SN  - 1546-0118

AB  - Accurate prediction of wind turbine power curve is essential for wind farm planning as it influences the expected power production. Existing methods require detailed wind turbine geometry for performance evaluation, which most of the time unattainable and impractical in early stage of wind farm planning. While significant amount of work has been done on fitting of wind turbine power curve using parametric and non-parametric models, little to no attention has been paid for power curve modelling that relates the wind turbine design information. This paper presents a novel method that employs artificial neural network to learn the underlying relationships between 6 turbine design parameters and its power curve. A total of 198 existing pitch-controlled and active stall-controlled horizontal-axis wind turbines have been used for model training and validation. The results showed that the method is reliable and reasonably accurate, with average R<sup>2</sup> score of 0.9966.
KW  - Wind turbine; power curve; artificial neural network; HAWT

DO  - 10.32604/EE.2021.014868