Wind turbine power estimation by neural networks with Kalman filter training on a SIMD parallel machine

Abstract

We use a multi-layer perceptron (MLP) network to estimate wind turbine power generation. Wind power can be influenced by many factors such as wind speeds, wind directions, terrain, air density, vertical wind profile, time of day, and seasons of the year. It is usually important to train a neural network with multiple influence factors and big training data set. We have parallelized the extended Kalman filter (EKF) training algorithm, which can provide fast training even for large training data sets. The MLP network is then trained with the consideration of various possible factors, which can influence turbine power production. The performance of the trained network is studied from the point of view of information presented to the network through network inputs regarding different affecting factors and large training data set covering all the seasons of a year.