Wind Turbine Power Estimation by Neural Networks with Kalman Filter Training on a SIMD Parallel Machine
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.
S. Li et al., "Wind Turbine Power Estimation by Neural Networks with Kalman Filter Training on a SIMD Parallel Machine," Proceedings of the International Joint Conference on Neural Networks, vol. 5, pp. 3430-3434, Institute of Electrical and Electronics Engineers (IEEE), Jan 1999.
The definitive version is available at http://dx.doi.org/10.1109/IJCNN.1999.836215
International Joint Conference on Neural Networks (IJCNN'99) (1999: Jul. 10-16, Washington, DC)
Electrical and Computer Engineering
International Standard Serial Number (ISSN)
Article - Conference proceedings
© 1999 Institute of Electrical and Electronics Engineers (IEEE), All rights reserved.