A Hybrid Approach Incorporating WSO-HO and the Newton-Raphson Method to Enhancing Photovoltaic Solar Model Parameters Optimisation
Accurate parameter estimation is vital for optimising the performance and design of photovoltaic (PV) systems. While metaheuristic algorithms (MHAs) offer promising solutions, they often face challenges such as slow convergence and difficulty balancing exploration and exploitation. This study introd...
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| Main Authors: | , , , |
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| Format: | Article |
| Language: | English |
| Published: |
Sciendo
2025-01-01
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| Series: | Power Electronics and Drives |
| Subjects: | |
| Online Access: | https://doi.org/10.2478/pead-2025-0003 |
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| Summary: | Accurate parameter estimation is vital for optimising the performance and design of photovoltaic (PV) systems. While metaheuristic algorithms (MHAs) offer promising solutions, they often face challenges such as slow convergence and difficulty balancing exploration and exploitation. This study introduces a novel hybrid approach, WSO-HO, which integrates the strengths of the war strategy optimization (WSO) and Hippopotamus Optimization (HO) algorithms, enhanced by the Newton-Raphson (NR) method, to achieve precise parameter estimation for PV models. The effectiveness of the WSO-HO algorithm was rigorously evaluated through intensive testing on three different solar panels, including the RTC France solar cell using the single diode model (SDM) and the double diode model (DDM), over 30 iterations. Comparative analysis highlights the superior performance of WSO-HO against conventional algorithms, which often struggle with accurately identifying PV model parameters. These promising results demonstrate the significant potential of this hybrid approach to improve parameter optimisation in PV systems, enabling more precise design and enhanced overall system efficiency. Furthermore, the simulation result of the performance of the WSO-HO algorithm was benchmarked against other algorithms reported in the literature, further validating its robustness and effectiveness. |
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| ISSN: | 2543-4292 |