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Experimental Investigation of the Hierarchical Control in DC Microgrids Using a Real-time Simulator

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

Maher G M Abdolrasol¹, Sieh Kiong Tiong², Pin Jern Ker³

¹Institute of Sustainable Energy, Universiti Tenaga Nasional, Kajang, 43000, Malaysia. maher.abdolrasol@uniten.edu.my.

Scientific Reports

|September 5, 2025

Summary

This study enhances solar energy conversion using a fuzzy-based pattern search (PS) optimized maximum power point tracking (MPPT) controller. It achieves superior efficiency and adaptability to changing conditions compared to traditional methods.

Keywords:

DC-DC boost converter MPPT Optimal fuzzy controller Pattern search optimization Photovoltaic Renewable energy

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Published on: September 12, 2014

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.