Enhanced Whale optimization algorithms for parameter identification of solar photovoltaic cell models: a comparative...

Area of Science:

• Renewable Energy Engineering
• Computational Intelligence
• Optimization Algorithms

Background:

• Accurate solar photovoltaic (PV) model parameter identification is essential for effective PV system design and performance analysis.
• The nonlinear current-voltage characteristics of PV cells present significant challenges for parameter estimation.
• Whale optimization algorithm (WOA) and its variants offer promising approaches for solving complex optimization problems like PV parameter identification.

Purpose of the Study:

• To conduct a comprehensive comparative analysis of the standard whale optimization algorithm (WOA) and ten enhanced WOA variants.
• To evaluate the performance of these algorithms in identifying five critical parameters of solar PV models.
• To determine the most effective WOA variant for tackling the intractable problem of PV parameter identification.

Main Methods:

• Application and comparison of WOA and ten enhanced WOA variants for identifying five PV model parameters.
• Utilizing evaluation indices such as solution accuracy, search robustness, and convergence curves to assess algorithm performance.
• Employing multi-model statistical analysis, including the Friedman test at a 0.05 confidence level, to rank the algorithms.

Main Results:

• The enhanced whale optimization algorithm (EWOA), which integrates a sorting-based differential mutation operator and Lévy flight strategy, achieved the top rank.
• EWOA demonstrated superior performance in terms of solution accuracy and search robustness compared to other tested WOA variants.
• Statistical analysis confirmed the significant performance differences among the evaluated algorithms.

Conclusions:

• EWOA is identified as the most effective algorithm for solar PV model parameter identification among the studied WOA variants.
• The study provides insights into the performance variations of different WOA enhancements for this specific problem.
• Recommendations for future research directions to further improve WOA for complex optimization tasks are presented.