MPPT FOR HYBRID PHOTOVOLTAIC/WIND/FUEL CELL POWER SYSTEM USING ARTIFICIAL NEURAL NETWORK

Abstract

Hybrid power systems new energy management approaches are covered in this article. An Artificial Neural Network (ANN) is used in the suggested management system in order to govern the flow of power between the hybrid powersystem to meet the demands of the system. To accomplishmaximumpowerpointtracking(MPPT) from a variety of energy sources, including photovoltaics (PV), wind turbines, fuel cells the neural network controller is used. Hybrid systems with PV panels, wind turbines (WTs), and fuel cells for hybrid system support with DC-DC converters are used to test the developed ANN-based approach. A control strategy is implemented with the ANN controller for smoothing the power fluctuation. Different operating conditions are used to test the proposed model's dynamic behavior. The proposed hybrid system provides more power than PV, WT, and FC systems atdifferentloads,accordingtotheanalysis.Forboth the stand-alone system and the grid, this research can be applied. For PV panels, wind turbines, and Fuel Cells with DC-DC converters for DC loads, the ANN performs better than Fuzzy in the MPPT approach. MATLAB/Simulink is used to simulate the results of the Fuzzy and ANNanalysis.