An Intelligent HG-GWO Framework for Performance Optimization of Grid-Connected Solar–Wind Hybrid Energy Generation

Abstract

Robust frameworks are needed to make the transition to sustainable energy, that make use of several renewable resources at once. This paper proposes a comprehensive study of a solar-wind hybrid power generation system optimized by Hybrid Grid-Based Grey Wolf Optimization (HG-GWO) algorithm which is integrated in the grid. The proposed architecture is organized by connecting a PV array subsystem, a wind turbine subsystem and power electronic converters to a utility grid, and a bidirectional DC-DC converter is used for seamless energy transfer. The Maximum Power Point Tracking (MPPT) algorithm integrated into HG-GWO framework ensures the PV subsystem always produces maximum output power under different irradiance and partial shading conditions. The controller is also regulating the active and reactive power to maintain the desired operating point of the power factor and provides protection of the grid interface from the disturbances from the external network. Combining Opposition Based Learning (OBL) with HG-GWO further increases the rate of convergence towards the Global Maximum Power Point (GMPP). The simulation results obtained in MATLAB validate the energy efficiency, grid stability and power quality gains over the conventional Perturbation and Observation (P&O) method.