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

Shuhui Li, Donald C. Wunsch, Edgar O'Hair, Michael G. Giesselmann

Research output: Contribution to conferencePaper

14 Scopus citations

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.

Original languageEnglish
Pages3430-3434
Number of pages5
StatePublished - 1999
EventInternational Joint Conference on Neural Networks (IJCNN'99) - Washington, DC, USA
Duration: Jul 10 1999Jul 16 1999

Conference

ConferenceInternational Joint Conference on Neural Networks (IJCNN'99)
CityWashington, DC, USA
Period07/10/9907/16/99

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