Enhancing the maximum power of wind turbine using artificial neural network

Bashar Mohammed Salih (Northern Technical University)
Khaleel Nawafal (Northern Technical University)
Safwan Assaf Hamoodi (Northern Technical University)

Bulletin of Electrical Engineering and Informatics
Vol 12, No 5: October 2023

Wind energy conversion systems (WECS) now play a significant role in meeting the world's energy needs. Several different approaches are used to try to increase the reliability of these renewable energy systems. Smart systems are designed to be more proactive to improve the performance of renewable energy equipment. Artificial neural networks (ANNs) have a variety of applications, including controlling renewable energy systems. Using optimal torque control (OTC) system based prediction techniques, a controller to monitor a maximum point of wind turbine output power (MPPT) is designed and modeled in this paper. MATLAB/Simulink package tools for neural networks are also used to design the controller and simulate it to achieve the necessary results and to obtain an appropriate analysis for the controller. The results show that the ANN is more active and delivers better output than the traditional controller.