Ripple Minimization in Renewable Energy Integration Improvement: Hybrid Intelligent Non-Linear Controller for PV/Wind/SAPF

The emergence of renewable energy sources and the need to optimize power quality in the grid have led to the development of new, sophisticated control approaches. These methods enable systems to supply nonlinear loads and inject excess energy into the grid. Photovoltaic and wind energy are integrated via an active shunt power filter (SAPF). The system injects excess energy into the grid while reducing total harmonic distortion (THD) and minimizing ripple in renewable energy production. This paper proposes hybrid techniques for maximum power point tracking (MPPT) for the PV and wind systems, which include the use of intelligent adaptive super sliding mode control (STSMC) based on deep artificial neural network (ANN) hybridization, a modified optimal relation based on model predictive control for the switching generation (ORB-MPC), and SAPF strategy control based on direct power with model predictive control (DPMPC). The results of the comparative study between conventional and proposed control approaches demonstrate significant superiority in maximizing renewable energy production, achieving efficiencies of 99.9% for PV systems and 99.3% for wind systems, with low fluctuations and minimized total harmonic distortion in the grid. This leads to rapid convergence of the DC link voltage, with settling times reduced by more than 20% compared to conventional approaches..