Parameters identification for photovoltaic module based on an improved artificial fish swarm algorithm.

Area of Science:

• Electrical Engineering
• Renewable Energy Systems
• Computational Intelligence

Background:

• Photovoltaic (PV) power systems require precise mathematical models for effective simulation and optimization.
• Traditional linear models are insufficient for capturing the complex nonlinearity and multiparameter nature of PV modules.
• Conventional parameter identification methods struggle with the intricacies of PV module modeling.

Purpose of the Study:

• To develop and validate an advanced optimization algorithm for accurate PV module parameter extraction.
• To address the limitations of existing methods in identifying parameters for nonlinear PV models.
• To enhance the performance of the Artificial Fish Swarm Algorithm (AFSA) for PV applications.

Main Methods:

• Implementation of the Artificial Fish Swarm Algorithm (AFSA), inspired by fish swarm behavior.
• Integration of a novel mutation operator (MO) to improve AFSA's search capabilities.
• Testing and validation of the proposed method across diverse PV module parameters and environmental conditions.

Main Results:

• The enhanced AFSA achieved higher precision in PV module parameter identification compared to conventional methods.
• The algorithm demonstrated efficient computational time for parameter extraction.
• Validation confirmed the method's feasibility under various operating conditions.

Conclusions:

• The proposed AFSA with a mutation operator offers a robust and accurate solution for PV module parameter identification.
• This method significantly improves the modeling and simulation accuracy of photovoltaic power systems.
• The findings contribute to the advancement of renewable energy system optimization and evaluation.