Optimized control of grid-connected photovoltaic systems: Robust PI controller based on sparrow search algorithm for smart microgrid application

Abstract

The integration of renewable energy sources into modern power systems necessitates efficient and robust control strategies to address challenges such as power quality, stability, and dynamic environmental variations. This paper presents a novel sparrow search algorithm (SSA)-tuned proportional-integral (PI) controller for grid-connected photovoltaic (PV) systems, designed to optimize dynamic performance, energy extraction, and power quality. Key contributions include the development of a systematic SSA-based optimization framework for real-time PI parameter tuning, ensuring precise voltage and current regulation, improved maximum power point tracking (MPPT) efficiency, and minimized total harmonic distortion (THD). The proposed approach is evaluated against conventional PSO-based and P&O controllers through comprehensive simulations, demonstrating its superior performance across key metrics: a 39.47% faster response time compared to PSO, a 12.06% increase in peak active power relative to P&O, and a 52.38% reduction in THD, ensuring compliance with IEEE grid standards. Moreover, the SSA-tuned PI controller exhibits enhanced adaptability to dynamic irradiance fluctuations, rapid response time, and robust grid integration under varying conditions, making it highly suitable for real-time smart grid applications. This work establishes the SSA-tuned PI controller as a reliable and efficient solution for improving PV system performance in grid-connected scenarios, while also setting the foundation for future research into multi-objective optimization, experimental validation, and hybrid renewable energy systems.