Performance Improvement of Grid-Connected PV-Wind Hybrid Systems Using Adaptive Neuro-Fuzzy Inference System and Fuzzy FOPID Advanced Control With OPAL-RT

This paper proposes, presented enhancing hybrid energy systems, specifically those combining photovoltaic (PV) and wind turbine sources, linked to the electrical grid with the use of an Adaptive Neuro-Fuzzy Inference System (ANFIS) control unit during implementation. Our study focuses on the comparative performance of the ANFIS controller against traditional Fuzzy Fraction-Order Proportional Integral-Derivative, (FOPID) controller. Hybrid energy systems present unique challenges due to renewable energy sources’ intermittent and non-linear nature. Conventional controllers, such as Fuzzy FOPID, often struggle to manage these complexities effectively. The ANFIS controller, nevertheless, blends fuzzy logic’s qualitative reasoning with neural networks’ capacity for adaptive learning, offering a more robust and flexible solution. Through extensive simulations and real-world testing, we demonstrate that the ANFIS controller significantly outperforms both Fuzzy FOPID controller in key performance metrics. These include improved voltage regulation, lower total harmonic distortion (THD), and enhanced overall system stability and efficiency under varying load and environmental conditions. The findings highlight ANFIS’s potential as a better hybrid energy system control method, enabling more dependable and effective grid integration of renewable energy sources. This research contributes to advancing smart grid technologies and promoting sustainable and resilient energy infrastructure.  The Simulation Results can be evaluated by using Matlab/Simulink Software.

Keywords: Adaptive neuro-fuzzy inference system, fuzzy FOPID, hybrid PV and wind turbine, hybrid system, photovoltaic (PV), wind turbine.