Exploration of Grid Integrated PV Inverter Using Ant Colony Optimization

The increasing penetration of photovoltaic (PV) systems into power grids has intensified the need for efficient, reliable, and intelligent control strategies for grid-integrated$\mathbf{P V}$inverters. Grid-connected PV inverters play a critical role in ensuring maximum power extraction, power quality improvement, voltage regulation, and stable interaction with the utility grid under varying environmental and load conditions. This paper presents an exploration of a grid-integrated PV inverter optimized using Ant Colony Optimization (ACO), a nature-inspired metaheuristic algorithm known for its robustness in solving complex, nonlinear optimization problems. The proposed approach applies ACO to optimize key inverter control parameters, enabling enhanced maximum power point tracking (MPPT), reduced harmonic distortion, improved dynamic response, and better grid synchronization. The study models a grid-connected PV system comprising a PV array, DC-DC converter, inverter, and grid interface, where ACO is employed to fine-tune control variables such as switching angles, modulation indices, and controller gains. Simulation-based analysis is conducted under diverse operating conditions, including irradiance fluctuations, temperature variations, and grid disturbances, to evaluate system performance. The results demonstrate that the ACO-optimized inverter achieves faster convergence to optimal operating points, improved power conversion efficiency, and lower total harmonic distortion compared to conventional control and optimization techniques. Additionally, the proposed method enhances system stability and ensures compliance with grid codes related to power quality and reactive power support.