Real-time experimental MPPT control of photovoltaic systems using Herd Horse Optimization

Photovoltaic (PV) systems are essential for the global energy transition, yet their efficiency is heavily dependent on the ability to capture solar energy under varying environmental conditions. Traditional Maximum Power Point Tracking (MPPT) methods often fail under partial shading conditions (PSC), which create multiple local maxima and lead to power losses through steady-state oscillations. Addressing this need for reliable and fast global tracking, this study implements a novel metaheuristic approach based on the Herd Horse Optimization (HHO) algorithm for Global MPPT. The research was conducted using a real-time experimental test bench consisting of a buck converter and an Arduino Mega board interfaced with MATLAB/Simulink, comparing HHO’s performance against Cuckoo Search (CS) and Flower Pollination (FPA) algorithms across three scenarios. Discoveries show that HHO achieves a superior conversion efficiency of 99.15% and reaches the maximum power point in an average of 0 . 45 s , which is significantly faster than the 0 . 54 s and 1 . 08 s required by CS and FPA, respectively. Furthermore, HHO demonstrated the lowest error metrics (RMSE, IAE, and ISE) and negligible steady-state oscillations across all tested conditions. These findings indicate that the HHO method provides an optimal balance between exploration and exploitation, effectively preventing entrapment in local optima. This research is useful and important as it provides a robust, high-performance foundation for improving the efficiency and stability of modern photovoltaic systems under complex, real-world operating conditions.