Assessment of Unknown Parameters in Photovoltaic Cells Utilizing Multi- Strategy Fusion Slime Mould Algorithm

Abstract

The study presents the Multi-Strategy Learning Fusion Slime Mould Algorithm (MFSMA), a novel method designed to optimize parameters in photovoltaic systems, thereby increasing the efficiency of solar energy conversion. MFSMA employs an advanced technique that incorporates elite opposition-based learning to quickly identify optimal solutions while preserving diversity within the population. Additionally, it integrates a ranking mechanism from the Grey Wolf Algorithm, which categorizes individuals according to their fitness levels, ensuring a balanced approach between exploratory diversity and exploitative precision during the optimization journey. Through rigorous testing on various benchmark functions, MFSMA has demonstrated its exceptional ability to outperform existing algorithms widely used in the sector. The algorithm’s effectiveness is further validated through its application in determining the parameters of single, double, and triple-junction photovoltaic modules. Moreover, the durability and effectiveness of the MFSMA algorithm have been rigorously evaluated using data from manufacturers’ datasheets across different temperature and irradiance conditions. Statistical analysis supports the conclusion that MFSMA offers superior accuracy and dependability in estimating vital parameters for photovoltaic modules, making it an invaluable tool for overcoming the challenges of parameter identification in solar energy technologies.

Keywords

• Photovoltaic models
• parameter extraction
• slime mould algorithm
• multi-strategy learning
• parameter optimization
• grey wolf algorithm