A chaotic self-adaptive JAYA algorithm for parameter extraction of photovoltaic models.

Area of Science:

• Renewable Energy Engineering
• Computational Intelligence
• Photovoltaic Systems Modeling

Background:

• Photovoltaic (PV) power generation systems require accurate modeling for optimal efficiency.
• PV model performance relies on parameter extraction, which is challenging due to nonlinear equations.
• Existing methods struggle with the accuracy and efficiency of PV model parameter extraction.

Purpose of the Study:

• To develop a novel algorithm for accurate and efficient parameter extraction in photovoltaic models.
• To improve the performance and competitiveness of photovoltaic power generation systems through enhanced modeling.

Main Methods:

• A chaotic self-adaptive JAYA algorithm (AHJAYA) was proposed, integrating self-adaptive coefficients.
• The algorithm combines linear population reduction with chaotic opposition-based learning to enhance convergence and avoid local optima.
• The performance of AHJAYA was validated using four distinct photovoltaic models.

Main Results:

• Experimental results demonstrated the superior performance of the AHJAYA algorithm.
• The proposed AHJAYA showed strong competitiveness compared to existing methods.
• The algorithm effectively addresses the challenges of nonlinear parameter extraction in PV models.

Conclusions:

• The AHJAYA algorithm offers a significant advancement in photovoltaic model parameter extraction.
• This improved accuracy contributes to higher efficiency in real-world photovoltaic power generation.
• AHJAYA presents a robust and competitive solution for PV system modeling and optimization.