Fuzzy controller-driven pattern search optimization for a DC-DC boost converter to enhance photovoltaic MPPT...

Area of Science:

• Renewable Energy Systems
• Power Electronics
• Intelligent Control Systems

Background:

• Solar energy systems require efficient Maximum Power Point Tracking (MPPT) to maximize energy harvest.
• Traditional MPPT algorithms face challenges in dynamic environmental conditions.
• Intelligent control techniques offer potential for improved MPPT performance.

Purpose of the Study:

• To develop and evaluate an intelligent MPPT controller using fuzzy-based pattern search (PS) optimization.
• To enhance energy conversion efficiency in DC-DC boost converters under varying irradiance and temperature.
• To compare the performance of the proposed fuzzy-PS MPPT controller against other optimization algorithms and the Perturb and Observe (P&O) method.

Main Methods:

• Implementation of a DC-DC boost converter with a fuzzy logic controller for MPPT.
• Optimization of fuzzy membership functions (MFs) using Pattern Search (PS) optimization.
• Comparative analysis with Particle Swarm Optimization (PSO) and Genetic Algorithm (GA) for fuzzy controller tuning, using Root Mean Square Error (RMSE) as the objective function.

Main Results:

• The fuzzy-PS optimization achieved the lowest RMSE (0.6861) after 100 iterations, outperforming fuzzy-GA (1.257) and fuzzy-PSO (0.9454).
• The proposed controller demonstrated effective adaptation to irradiance and temperature variations, reaching maximum power outputs up to 74.48 kW.
• An average MPPT efficiency of 99.7% was achieved, significantly outperforming the P&O algorithm.

Conclusions:

• The fuzzy-PS optimized MPPT controller offers superior tracking performance and energy conversion efficiency.
• The intelligent control strategy effectively handles dynamic changes in solar irradiance and temperature.
• This approach represents a significant advancement in optimizing solar energy harvesting systems.