Wind energy conversion systems appear as an attractive alternative for electricity generation. To maximize the use of wind generated electricity when connected to the electric grid, it is important to estimate and predict power produced by wind farms. The power generated by electric wind turbines changes rapidly because of the continuous fluctuation of wind speed and wind direction. Wind power can be affected by many other factors such as terrain, air density, vertical wind profile, time of a day, and seasons of a year and usually fluctuates rapidly, imposing considerable difficulties on the management of combined electric power systems. It is important for the power industry to have the capability to perform this prediction for diagnostic purposes-lower than expected wind power may be an early indicator of a need for maintenance. A multilayer perceptron (MLP) network can be used to estimate wind turbine power generation. It is usually important to train a neural network with multiple influence factors and big training data set. The extended Kalman filter training algorithm has to be parallelized so that it can provide fast training even for large training data sets. The MLP network can then be trained with the consideration of various possible factors, which can cause influence on turbine power production.