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^{2} score of 0.9966.

Modelling of wind turbines is important in windfarm planning for accurate prediction of power production [

Conventional methods based on Blade Element Momentum (BEM) theory [

In recent years, parametric and non-parametric regression methods have received a lot of attention for wind turbine power curve modelling for on-site condition monitoring of wind turbines. Parametric models such as polynomials [

To the authors’ knowledge, there is no method that would produce a power curve for a given set of basic wind turbine design information such as turbine diameter, rated speed, etc. In view of this, this work is dedicated to close the loop by formulating a modelling architecture that relates the design information and its power production using ANN. The remaining of the paper is organized as follows: a brief review of ANN and assumptions used in developing the model are described in Section 2, followed by results and discussion in Section 3, and conclusions are presented in Section 4.

Each neuron is modelled as depicted in

where,

The first term of the objective function is Mean Squared Error (MSE) of the model and targeted outputs for all

A total of one hundred and ninety-eight existing pitch-controlled and active stall-controlled wind turbines consisted of two and three blades have been considered in this study. The wind turbines are split into 7:3 ratio by random for ANN training and testing, respectively. All wind speeds of the wind turbine power curves (^{3} using

To further improve the model generalization of ANN, a total of ten independent ANN models have been created, as depicted in

R^{2} was used to quantify the performance of the proposed method. The R^{2} value for the cross-validation plot (^{2} is 0.9996 for Vensys 70 1.5 MW wind turbine (see ^{2} shows that the proposed method is accurate. The proposed method tends to predict constant power in the speed range of

However, a close examination on

In this work, a simple MLP ANN method for power curve estimation has been proposed. The proposed method receives 6 basic wind turbine design information (^{2} score of 0.9966. However, the proposed method performs poorly for HAWTs equipped with

In conventional wind turbine design, detailed geometry of the wind turbine has to be generated before its aerodynamic properties and power curve can be evaluated using physics-based methods such as BEM or high-fidelity numerical simulations such as Computational Fluid Dynamics. In the present study, historical data of wind turbines have been used to generate a low fidelity model using ANN. Unlike the existing parametric and non-parametric power curve modelling methods for on-site condition monitoring of wind turbines, the method proposed in this study is the best-known method to estimate the power curve without having to account for detailed wind turbine geometry and its aerodynamic properties, hence it provides a quick means for applications such as wind turbine design optimisation as well as windfarm planning and windfarm optimisation.