Integration of Solar Photovoltaic with Modular Multiport Converter Using a Pi Controller Optimized Through Hybrid Osprey Optimization Algorithm and Relational Bi-Level Aggregation Graph Network

The integration of solar photovoltaic (SPV) systems with modular multiport converters (MMPC) enables efficient energy conversion and distribution, enhancing the overall performance and reliability of renewable energy systems (RES). However, the complexity of the control algorithms and potential issues related to the dynamic response can pose challenges in achieving optimal performance and stability in varying operating conditions. This paper proposes a hybrid method for integrating SPV systems with MMPC to achieve efficient power management in modern renewable energy grids. The proposed hybrid method is the combined execution of the Osprey Optimization Algorithm (OOA) and Relational Bi-level Aggregation Graph Convolutional Network (RBAGCN). Hence it is named as OOA-RBAGCN technique. The aim is to ensure optimal power transfer, minimize total harmonic distortion (THD), maintain voltage stability under dynamic operating conditions, and ultimately improve the overall energy efficiency, reliability, and performance of SPV-based RES within smart grid applications. The OOA is used to optimize the control parameter of the proportional-integral (PI) controller. The RBAGCN is used to predict these optimized parameters. By then, the proposed approach is used on the MATLAB platform and compared with other approaches such as Starling Murmuration Optimization (SMO), Dung Beetle Optimizer (DBO), Improved Harris Hawks Optimization (IHHO), Grey Wolf Optimization (GWO), and Particle Swarm Optimization (PSO). The proposed method achieves a high efficiency of 98.1%, and a reduced THD of 2.9% significantly surpassing all existing methods.