Title :
Neural network for wind power generation with compressing function
Author :
Li, Shuhui ; Wunsch, Don C. ; O´Hair, Edgar ; Giesselmann, Michael G.
Author_Institution :
Dept. of Electr. Eng., Texas Tech. Univ., Lubbock, TX, USA
Abstract :
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 estimate this changing power. In this paper, the characteristics of wind power generation are studied and a neural network is used to estimate it. We use real wind farm data to demonstrate a neural network solution for this problem, and show that the network can estimate power even in changing wind conditions
Keywords :
backpropagation; feedforward neural nets; parameter estimation; power engineering computing; wind power plants; wind turbines; backpropagation; compressing function; electric wind turbines; forecasting; multilayer neural networks; power estimation; wind power generation; Meteorology; Neural networks; Poles and towers; Power generation; Power measurement; Wind energy generation; Wind farms; Wind power generation; Wind speed; Wind turbines;
Conference_Titel :
Neural Networks,1997., International Conference on
Conference_Location :
Houston, TX
Print_ISBN :
0-7803-4122-8
DOI :
10.1109/ICNN.1997.611648
