Open Access

ARTICLE

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

Vin Cent Tai^1,*, Yong Chai Tan¹, Nor Faiza Abd Rahman¹, Chee Ming Chia², Mirzhakyp Zhakiya², Lip Huat Saw³

1 Centre for Modelling and Simulation, Faculty of Engineering, Built Environment and Information Technology, SEGi University, Petaling Jaya, 47810, Malaysia
2 Mechanical Engineering Department, Faculty of Engineering, Built Environment and Information Technology, SEGi University, Petaling Jaya, 47810, Malaysia
3 Lee Kong Chian Faculty of Engineering and Science, UTAR, Kajang, 43200, Malaysia

* Corresponding Author: Vin Cent Tai. Email:

(This article belongs to the Special Issue: The Role of Artificial Intelligence for Modeling and Optimizing the Energy Systems )

Energy Engineering 2021, 118(3), 507-516. https://doi.org/10.32604/EE.2021.014868

Received 04 October 2020; Accepted 13 January 2021; Issue published 22 March 2021

Abstract

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 R² score of 0.9966.

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Keywords

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