An Improved Elk Herd Optimization Algorithm for Maximum Power Point Tracking in Photovoltaic Systems Under Partial...

Area of Science:

• Renewable Energy Systems
• Photovoltaic Power Conversion
• Optimization Algorithms

Background:

• Partial shading conditions (PSCs) in photovoltaic (PV) systems create multiple peaks in power-voltage characteristics.
• Traditional maximum power point tracking (MPPT) algorithms often get trapped in local optima, reducing energy conversion efficiency.
• Rapidly and effectively locating the global maximum power point under complex environmental conditions is crucial for PV performance.

Purpose of the Study:

• To propose an improved elk herd optimization (IEHO) algorithm for rapid global maximum power point tracking (MPPT) in PV systems under various weather conditions.
• To enhance MPPT performance and energy conversion efficiency in PV systems operating under partial shading.

Main Methods:

• Developed an improved elk herd optimization (IEHO) algorithm incorporating a position update mechanism guided by predation risk probability.
• Introduced a triangle walk strategy to improve the algorithm's ability to escape local optima.
• Implemented a memory-guided redirection strategy to optimize convergence speed by skipping redundant historical duty cycle calculations.

Main Results:

• The IEHO algorithm demonstrated superior performance compared to other meta-heuristic algorithms across various weather conditions.
• Achieved an average tracking efficiency of 99.99% under tested conditions.
• Attained an average tracking time of 0.3886 seconds, indicating significant improvement in convergence speed.

Conclusions:

• The proposed IEHO algorithm effectively addresses the challenge of local optima in MPPT under partial shading.
• IEHO significantly enhances the speed and accuracy of global maximum power point tracking in photovoltaic systems.
• The algorithm offers a promising solution for improving the overall energy conversion efficiency of PV systems in dynamic environments.