Analysis of Wind Energy System Using Neural Network Controller

This paper illustrates the technique to extract maximum power utilizing wind energy present in the environment, voltage stability, and wind speed estimation for a permanent magnetic synchronous generator (PMSG). A wind turbine-based conversion system has been implemented with the neural network controller under varying wind speed conditions present in the environment. Results show that without neural network predictive controller (NNPC) generation from a wind turbine is less due to environmental wind fluctuations which also provide fluctuation in voltage. The neural network is trained by utilizing a neural network controller optimization algorithm for the better performance of the permanent magnetic synchronous generator (PMSG). Power and voltage variations due to changes in the environmental conditions of wind are taken into account along with the comparison of present and previous changes in the system. This control method optimizes the maximum power from the wind energy conversion system with fewer iterations along with the utilization of the neural network predictive controller (NNPC). The steady state and dynamic response of the wind energy conversion system (WECS) is analyzed using MATLAB Simulink.