Power Quality Optimization in PV Grid Systems Using Hippopotamus-Driven MPPT and SyBel Inverter Control

In grid-connected photovoltaic systems, improving power quality is necessary for assuring constant energy delivery, consistent voltages, and current, as well as being compliant with the standards of the grid. Yet, today’s PV control systems have to deal with serious problems, for example, slow MPPT reactions to changes in irradiation, significant harmonic distortion, weak reaction to voltage changes, and being unable to adapt well to different situations. For this reason, these problems lead to less efficient electricity, unstable connections to the power grid, and an altered quality of electricity, as solar power and load levels vary in real conditions. A way to solve these problems is introduced in this paper: (1) the Hippopotamus-based Solar Power MPPT Tracker and (2) a SyBel embedded controller for controlling the inverter. This kind of optimization mimics nature to control the duty cycle and enables the boost converter to deliver maximum power while responding quickly and maintaining accurate tracking. Meanwhile, the SyBel controller makes use of a hybrid technique by using SNN, DBN, and synergetic logic to sensibly manage the inverter switches and increase the power quality. The framework is novel because it uses biological optimization plus deep learning-based embedded control to instantly handle error reduction and harmonic suppression. The whole process records energy from solar panels, follows the maximum power point, changes its schedule as needed, and uses sophisticated controls in the inverter. We found that the proposed MPPT tracker achieves an impressive tracking efficiency of 98.6%, surpassing PSO, FLC, and ANFIS, and lowering the time required for tracking by 72%. The SyBel inverter controller provides outstanding results, keeping the voltage THD at 1.2% and current THD at 1.3%, which matches power quality standards.