Enterprise AI Analysis
Enhanced Maximum Power Point Tracking Using Hippopotamus Optimization Algorithm for Grid-Connected Photovoltaic System
Authors: Salah A. Taha, Mohammed Abdulla Abdulsada, Mohamed Ahmed Ebrahim Mohamed, Mohammed Alruwaili & Ahmed Emara
Publication Date: February 20, 2026
Executive Impact Summary
This study introduces an advanced maximum power point tracking (MPPT) control strategy for a grid-connected photovoltaic (PV) system, leveraging the novel Hippopotamus Optimization Algorithm (HOA) to optimally tune Fractional-Order Proportional-Integral (FOPI) controllers. Benchmarked against Arithmetic Optimization Algorithm (AOA) and Grey Wolf Optimizer (GWO), the HOA-based FOPI-IC-MPPT configuration demonstrates superior dynamic performance in a 100 kW grid-tied PV system. Key results include a minimum rise time of 0.0073 s and a maximum extracted power of 100.72 kW. The proposed method significantly reduces rise time by 9.88% and settling time by 19.73% under the IAE criterion compared to AOA and GWO, establishing a better trade-off between dynamic response and tracking accuracy, making it a promising solution for real-time PV applications.
Keywords: Photovoltaic (PV), grid-tied inverter, MPPT, Hippopotamus Optimization Algorithm (HOA), Grey Wolf Optimizer (GWO), active power
Reduction in Rise Time (HOA vs. AOA/GWO)
Reduction in Settling Time (HOA vs. AOA/GWO)
Max Power Extracted by HOA
Minimum Rise Time by HOA
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Advanced MPPT Optimization with HOA
This section explores the core contributions of the research, focusing on the novel application of the Hippopotamus Optimization Algorithm (HOA) for tuning Fractional-Order Proportional-Integral (FOPI) controllers within an Incremental Conductance (IC) Maximum Power Point Tracking (MPPT) framework for grid-connected photovoltaic systems.
0.0073s
Minimum Rise Time Achieved (HOA-FOPI-IC-MPPT)
100.72kW
Maximum Power Extracted (HOA-FOPI-IC-MPPT)
HOA Algorithm Process
Initialize Population & Parameters
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Evaluate Objective Function & Identify Dominant Solution
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Apply Exploration Phase (Herd Behavior)
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Detect Local Trap? (Yes: Apply Defense Phase / No: Skip)
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Apply Exploitation Phase (Escape Movement)
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Recalculate Fitness of Updated Solutions
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Check Max Iterations? (Yes: Display Optimal Solution / No: Repeat)
| Algorithm | Max Power (kW) | Power Loss (%) | Efficiency (%) | Tracking Error | Implementation Cost |
|---|---|---|---|---|---|
| PSO | 98.7 | 1.3 | 98.7 | Medium | Medium |
| GWO | 99.1 | 0.9 | 99.1 | Low | Medium |
| AOA | 99.4 | 0.6 | 99.4 | Low | Medium |
| HOA | 99.7 | 0.3 | 99.7 | Very Low | Low |
Scenario 1: Constant Temperature, Step Irradiance
Under constant temperature and a step change in solar irradiance (Figures 18 & 19), HOA-based FOPI-IC-MPPT demonstrates superior dynamic response (Figures 20 & 21). It reduces rise time by 61% and settling time by 94% compared to MIC (implied as a baseline or poor performer), GWO, and AOA. HOA also exhibits a smoother PV voltage profile and robust DC-link voltage regulation (Figures 22 & 25).
Scenario 2: Ramp Irradiance, Constant Temperature
With a ramp-like varying solar irradiance and constant temperature (Figures 26 & 27), HOA delivers quicker dynamic responses than both GWO and AOA (Figures 28 & 29). The minimum PV power achieved by HOA is 99.9kW, outperforming AOA (99.5kW) and GWO (93kW), particularly under low-irradiance conditions, demonstrating superior steady-state and transient performance (Figures 30 & 31).
Scenario 3: Varying Irradiance Levels, Constant Temperature
Under constant temperature with different stepwise solar irradiance levels (Figures 32 & 33), all algorithms successfully tracked the MPP. However, HOA achieved the best performance during transient periods (e.g., t=1.2:1.3s), followed by AOA and GWO (Figure 34). HOA-based PV voltage shows the highest smoothness and stability, with a minimum voltage of 270.3V (Figures 35-38).
Scenario 4: Variable Temperature & Solar Irradiance
In the most challenging scenario, involving simultaneous variations in temperature and solar irradiance (Figures 39 & 40), the HOA algorithm achieved the fastest power tracking performance, followed by AOA and GWO (Figure 41). The PV voltage decreases with increasing temperature, highlighting the inverse relationship, and HOA maintains robust DC-link voltage and grid current stability (Figures 42-45).
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