Adaptive Neuro-Fuzzy Control for Grid-Connected Renewable Energy Systems

Power electronics plays a crucial role in controlling grid-connected renewable energy sources. This paper presents a novel adaptive neuro-fuzzy control approach for renewable interfacing inverters, with the main objective of achieving smooth bidirectional power flow and nonlinear unbalanced load compensation. The proposed method combines neural network learning capabilities with fuzzy logic reasoning to handle system uncertainties. The implementation involves developing adaptive rule bases and membership functions that dynamically adjust to changing operating conditions, providing superior performance compared to conventional PI controllers in highly nonlinear systems.