Multilayer Artificial Neural Network based Nano Grid of Hybrid PV/Wind and Energy Storage

In the current scenario, fossil fuel-based electricity generation pollutes the environment and contributes to climate change. Photovoltaic (PV) and wind energy systems are encouraged to generate power, reduce pollution, and protect the Earth from climate change. This research paper focused on designing and modeling a N ano Grid using a hybrid Photovoltaic/Wind and battery storage system. The PV and wind energy systems are entirely reliant on natural resources. The PV and wind energy systems are entirely dependent upon natural resources. The generation of maximum electricity from PV and wind energy systems is a significant problem due to weather conditions and dependability. The MPPT algorithm is a critical part of the renewable energy generation process when it comes to delivering maximum power in non-linear weather circumstances. This paper proposes a new multi-layer artificial neural network algorithm (MANN) for MPPT of PV and wind energy systems. The power generation of PV and wind energy systems is unreliable due to sunlight and wind flow availability. One of the essential benefits for power producers is consistent power delivery to consumers. The proposed MANN controller was used to model and control the energy storage system in this study. Finally, the proposed nano grid model was simulated in Matlab, and the simulation results were analyzed to demonstrate the system's efficacy.