Global MPPT optimization for partially shaded photovoltaic systems

Area of Science:

• Renewable Energy Systems
• Optimization Algorithms
• Power Electronics

Background:

• Growing global energy demand and climate change necessitate efficient power system solutions.
• Solar photovoltaic (PV) systems face challenges in maximizing power output, especially under partial shading conditions.
• Conventional analytical methods for Maximum Power Point Tracking (MPPT) are computationally intensive.

Purpose of the Study:

• To investigate and compare the performance of recent heuristic optimization algorithms for solar PV systems.
• To address the complexities of maximizing power output under partial shading conditions.
• To identify an efficient algorithm for achieving the Global Maximum Power Point (GMPP).

Main Methods:

• A single-objective non-linear optimization problem was formulated and solved.
• Particle Swarm Optimization (PSO), Cat Swarm Optimization (CSO), Teaching Learning Based Optimization (TLBO), Grey Wolf Optimization (GWO), and Chimp Optimization algorithm (ChOA) were employed.
• Simulations were conducted in a MATLAB/SIMULINK environment.

Main Results:

• The Chimp Optimization algorithm (ChOA) achieved the highest conversion efficiency (99.63%) and maximum power output (525.13 W).
• ChOA demonstrated faster convergence and easier implementation compared to other tested algorithms.
• ChOA effectively reduced power oscillations and achieved precise MPP convergence.

Conclusions:

• Heuristic algorithms, particularly ChOA, offer efficient solutions for optimizing solar PV system performance under partial shading.
• ChOA outperforms established methods in GMPP search, providing a robust approach for solar energy utilization.
• The findings support the use of advanced optimization techniques for enhancing the reliability and efficiency of solar power systems.