Adaptive optimization and harmonic management in grid-connected hybrid solar PV systems

The growing penetration of solar photovoltaic (PV) systems into the grid presents issues of energy degradation, harmonic distortion, and dynamic unsteadiness, especially when there is a fluctuation in irradiance, nonlinear load, and weak grids. The conventional methods of control and filtering are typically either appropriate to one or the other of maximum power extraction versus harmonic suppression, and thus would not be useful in practice. In the paper below, a suggestion is made to use dense forced reinforcement learning (DEL-HAPF) for hybrid active power filters in utility-related PV systems. The scheme supports a DEL-based maximum power point tracking (MPPT) scheme with a hybrid electrical conditioning system comprising shunt active, series active, and passive filters, with a three-level neutral point clamped (NPC) inverter to support it. The reinforcement learning controller enables it to adjust to variable irradiance, load variations, and grid perturbations in real time, optimizing power collection and minimizing harmonics. Performance is measured in terms of total harmonic distortion (THD), grid stability, power output, dynamic response, and overall effectiveness in MATLAB/Simulink. DEL-HAPF outperforms both conventional and advanced strategies in simulations, with lower THD, higher efficiency, faster response, and better output. The findings demonstrate that DEL-HAPF is a smart, versatile, and scalable technology for improving energy consumption and power quality in grid-connected PV systems, particularly in weak or stressed grids with high levels of renewable integration.