Neural Network MPPT Control of an On-Grid Wind Energy System

Variable speed functioning of the wind turbine offers the possibility to produce the maximum available power over an extensive range of wind blowing speed variation. So, the electrical energy extracted from the turbine can be essentially maximized by applying an MPPT control. This work focuses on applying MPPT neural network control (NNC) to the boost converter (DC-to-DC) part of the wind power conversion system with variable speed linked to the grid. The chain’s power output varies depending on the speed of its generator, which in our case is a synchronous machine featuring permanent magnets (PMSM). Regarding the voltage tracking of the DC-to-DC booster converter stays handled over the use of a neural network controller. Simulation and analysis of the NNC technique and perturb-and-observe (P&O) technique were performed so as to evaluate and validate efficacy of each respective approach. The MATLAB–SIMULINK block diagram environment is employed for the simulation of the entire system. The suggested strategy (NNC) offers the possibility for enhanced profitability in the energy conversion chain. The proposed approach’s efficiency and accuracy are assessed on the basis of simulation results.