Inverter Control using ANN in Grid linked PV, Wind & Battery system

Abstract

This research investigates the implementation of an Artificial Neural Network (ANN) control strategy for a hybrid grid-linked scheme that integrates PV, wind, and battery energy sources. As the demand for renewable energy solutions grows, the focus on hybrid systems that leverage the strengths of multiple energy sources is critical for enhancing stability and efficiency in power generation. The study compares the performance of the proposed ANN controller to control techniques, such as Proportional-Integral (PI) and Fuzzy Logic Controllers , in managing the dynamic interactions among the energy sources and ensuring optimal power output under varying environmental conditions. A MATLAB Simulink model was developed, which utilized P&O MPPT to achieve an efficiency of 98.5%.The results demonstrate that the ANN controller outper-forms both PI and Fuzzy controllers in maintaining a stable DC link voltage of approximately 600V, thereby enhancing grid synchronization and power quality during fluctuating condi-tions.This research contributes valuable insights towards the design of more resilient and efficient grid-connected renewable energy systems, highlighting the potential of ANN-based control methods in managing complex energy generation and storage dynamics