Modified Artificial Hummingbird Algorithm-Based Single-Sensor Global MPPT for Photovoltaic Systems

Recently, a swarm-based method called Artificial Hummingbird Algorithm (AHA) has been proposed for solving optimization problems. The AHA algorithm mimics the unique flight capabilities and intelligent foraging techniques of hummingbirds in their environment. In this paper, we propose modified version combined with genetic operators mAHA. experimental results show that mAHA improved convergence speed achieved better effective search results. Consequently, was used first time to find global maximum power point (MPP). Low efficiency is drawback photovoltaic (PV) systems explicitly use shading. Normally, PV characteristic curve an MPP when irradiance uniform. Therefore, can be easily conventional tracking systems. With shadows, however, conditions are completely different, multiple MPPs (i.e., some local single MPP). Traditional approaches cannot distinguish between MPPs, thus simply get stuck at MPP. optimized MPPT metaheuristic required determine Most require more than one sensor, e.g., voltage, current, irradiance, temperature sensors. This increases cost control system. current research, simple only sensor considering shadow conditions. Two scenarios considered evaluate superiority obtained based