A High-Performance MPPT Solution for Solar DC Microgrids: Leveraging the Hippopotamus Algorithm for Greater Efficiency and Stability

The rapid growth of modern civilization has led to increased global warming and climate challenges. Variations in atmospheric temperature, sunlight intensity and other factors significantly impact the performance of photovoltaic (PV) systems. To maximize energy production, these systems must operate efficiently at their Maximum Power Point under varying weather conditions. This study introduces a new Hippopotamus Algorithm (HA) designed for Maximum Power Point Tracking (MPPT) in solar PV systems connected to direct current (DC) microgrids. Performance of HA's is compared with three established optimization algorithms: Grey Wolf Optimization, Cuckoo Search Algorithm and Particle-Swarm Optimization across different operating scenarios and partial shading circumstances. Obtained results demonstrate that the HA not only achieves higher power output but also responds faster than existing methods. In each of the partial shading conditions, the efficiency range of proposed methods are 82.16% and 89.92%, respectively, and in the temperature variation case the efficiency is 84.67% which is far better than the other three approaches. As per stability concerns, the proposed HA-based MPPT approach attains minimal settling time and gives steady-state stable output power to its load in both partial shading, temperature fluctuation and steady-state conditions. A comparative analysis is also shown in tabular form in this article. Additionally, it effectively manages bidirectional power flow in both stable and fluctuating weather conditions. This approach ensures a resilient and sustainable architecture for low power generating situations when a DC microgrid is integrated with an HA-based MPPT system.