Wind turbine power curve modelling using artificial neural network

Highlights

Power curve modelling difficult to achieve through parametric functions.

Power curve modelling via ANN with 6 inputs shown to have low level of absolute and random errors.

Most important inputs: wind speed, air density, TI, shear, direction, yaw error.

Most adapted topology is ANN with four neurons on 1st layer and one on 2nd layer.

No strong correlation between TI and wind shear or other inputs.

Abstract

Technical improvements over the past decade have increased the size and power output capacity of wind power plants. Small increases in power performance are now financially attractive to owners. For this reason, the need for more accurate evaluations of wind turbine power curves is increasing. New investigations are underway with the main objective of improving the precision of power curve modeling. Due to the non-linear relationship between the power output of a turbine and its primary and derived parameters, Artificial Neural Network (ANN) has proven to be well suited for power curve modelling. It has been shown that a multi-stage modelling techniques using multilayer perceptron with two layers of neurons was able to reduce the level of both the absolute and random error in comparison with IEC methods and other newly developed modelling techniques. This newly developed ANN modeling technique also demonstrated its ability to simultaneously handle more than two parameters. Wind turbine power curves with six parameters have been modelled successfully. The choice of the six parameters is crucial and has been selected amongst more than fifty parameters tested in term of variability in differences between observed and predicted power output. Further input parameters could be added as needed.

Keywords

  • Wind turbines;
  • Power curve modelling;
  • Artificial neural network;
  • Air density;
  • Turbulence intensity;
  • Wind shear

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