An enhanced adaptive comprehensive learning hybrid algorithm of Rao-1 and JAYA algorithm for parameter extraction of...

Area of Science:

• Renewable Energy Engineering
• Computational Intelligence
• Materials Science

Background:

• Photovoltaic (PV) system efficiency relies on accurate parameter extraction from PV models.
• Traditional methods struggle with the nonlinear equations inherent in PV models.
• Accurate parameter identification is essential for optimizing solar energy acquisition.

Purpose of the Study:

• To propose an enhanced hybrid JAYA and Rao-1 algorithm (EHRJAYA) for efficient and accurate PV model parameter extraction.
• To improve upon traditional analytical and key point methods for parameter identification.
• To enhance the performance of optimization algorithms in the context of photovoltaic modeling.

Main Methods:

• Developed an enhanced hybrid algorithm (EHRJAYA) by combining JAYA and Rao-1 algorithms.
• Implemented an improved comprehensive learning strategy with differential selection probabilities for individuals.
• Incorporated adaptive coefficient strategies and a dynamic lens opposition-based learning strategy.
• Utilized a linear population reduction strategy to enhance convergence and escape local optima.

Main Results:

• The proposed EHRJAYA algorithm demonstrated superior performance in parameter extraction.
• EHRJAYA achieved leading performance compared to other well-known optimization algorithms.
• Experimental results validated the effectiveness and accuracy of the EHRJAYA approach.

Conclusions:

• The EHRJAYA algorithm offers a significant advancement in extracting unknown parameters for photovoltaic models.
• The enhanced hybrid approach improves population diversity, information utilization, and convergence speed.
• This method provides a more efficient and accurate solution for optimizing photovoltaic system performance.