Title

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 a day, and seasons of a 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 cause influence on 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 to different affecting factors and large training data set covering all the seasons of a year.

Meeting Name

International Joint Conference on Neural Networks (IJCNN'99) (1999: Jul. 10-16, Washington, DC)

Department(s)

Electrical and Computer Engineering

International Standard Serial Number (ISSN)

1098-7576

Document Type

Article - Conference proceedings

Document Version

Citation

File Type

text

Language(s)

English

Rights

© 1999 Institute of Electrical and Electronics Engineers (IEEE), All rights reserved.


Share

 
COinS