Using neural networks to estimate wind turbine power generation

This paper uses data collected at Central and South West Services Fort Davis wind farm (USA) to develop a neural network based prediction of power produced by each turbine. The power generated by electric wind turbines changes rapidly because of the continuous fluctuation of wind speed and direction. It is important for the power industry to have the capability to perform this prediction for diagnostic purposes-lower-than-expected wind power may be an early indicator of a need for maintenance. In this paper, characteristics of wind power generation are first evaluated in order to establish the relative importance for the neural network. A four input neural network is developed and its performance is shown to be superior to the single parameter traditional model approach.

I. Introduction

Advances in wind turbine technology [1] and rich wind resources in many areas improve prospects for the wind power industry [2], which motivates the analysis of wind power generation and wind turbine performance. However, because turbines are distributed over a wide area in a wind farm, the power generated by each is usually different due to variations in wind direction and speed. In fact, measured wind data is seldom identical to that seen at the generator for a variety of reasons, including the expense of obtaining and maintaining that data, and topographical constraints.