Enhanced maximum power point tracking using hippopotamus optimization algorithm for grid-connected photovoltaic system

In this study, an advanced maximum power point tracking (MPPT) control strategy is proposed for a grid-connected photovoltaic (PV) system using the Hippopotamus Optimization Algorithm (HOA). The Incremental Conductance (IC) MPPT technique is integrated with three control approaches: Integral (I), Proportional-Integral (PI), and Fractional-Order Proportional-Integral (FOPI) controllers. The HOA is employed to optimally tune the controller parameters, and its performance is benchmarked against two other nature-inspired algorithms: the Arithmetic Optimization Algorithm (AOA) and the Grey Wolf Optimizer (GWO). A 100 kW grid-tied PV system connected to a medium-voltage distribution network is modeled and simulated in MATLAB/Simulink 2025a. The optimization process aims to minimize four classical performance indices: IAE, ISE, ITAE, and ITSE. Simulation results demonstrate that the HOA-based FOPI-IC-MPPT configuration achieves superior dynamic performance, exhibiting a minimum rise time of 0.0073 s and a maximum extracted power of 100.72 kW. Under the IAE criterion, compared to AOA and GWO, the proposed method reduces the rise time by 9.88% and the settling time by 19.73%. Although the GWO-based controller outperformed in certain metrics (e.g., ISE), the HOA-based approach achieved a better trade-off between dynamic response and maximum power tracking accuracy, making it a promising solution for real-time grid-connected PV applications under variable environmental conditions.