Adaptive hybrid particle swarm optimization and fuzzy logic controller for a solar-wind hybrid power system

Abstract

This paper presents the best modeling and control strategies for a grid-connected hybrid wind-solar power system to maximize energy production. For variable wind speeds, determine the optimal power point using fuzzy logic control, adopt an adaptive hill climb searching method, and compare it with an optimal torque control method for large inertia wind turbine (WT). The role of fuzzy logic controller (FLC) is to adjust the hill climbing search (HCS) technique's step-size according to the operating point. The doubly-fed induction generator (DFIG) control system has two subsystems: rotor-side and grid-side converters. The active and reactive power have been indirectly regulated by adjusting the current on the d-q axis. The rotor side converter (RSC) controllers are responsible for controlling the WTs rotational speed to achieve the maximum power output. The grid side converter (GSC) manages the voltage at the DC link and keeps a unity power factor between the grid and GSC. Optimal hybrid power point tracking technique for use with photovoltaic systems in both constant and variable shade circumstances, based on particle swarm optimization (PSO) and perturb and observe (P&O). The optimal power point tracking (OPPT) approach is compared to three other methods: PSO, P&O, and hybrid P&O-PSO. The model has a total capacity of 2.249 MW, with wind capacity of 2 MW and solar capacity of 0.249 MW, and its efficiency is analyzed.