Enhancing the maximum power of wind turbine using artificial neural network

Abstract

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.

Enhancing the Behavior of Small Scale Wind Turbine Based on Fuzzy Logic System

“…This principle governs wind turbine power control. Mechanical output power is another variable in the case of wind turbines [12,13].…”

Optimal placement of wind turbine in distribution grid to minimize energy loss considering power generation probability

“…For wind turbine, there is a lite difference from other DGs. The wind turbine output cannot remain at the rated value during operating interval because the power generation of wind turbine is heavily depending on wind speed [20]- [22]. Therefore, the wind speed variation has an impact on the power loss of the connected grid.…”