用于DC-DC升压转换器的模糊控制器驱动模式搜索优化，以提高光伏最大功率点跟踪性能。

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

作者信息

Abdolrasol Maher G M, Tiong Sieh Kiong, Ker Pin Jern, Ansari Shaheer, Ayob Afida, Shuaibu Hassan Abdurrahman, Busaidi Ahmed Said Al, Ustun Taha Selim

机构信息

Institute of Sustainable Energy, Universiti Tenaga Nasional, Kajang, 43000, Malaysia.

Faculty of Engineering and Technology, Sunway University, Bandar Sunway, Petaling Jaya, 47500, Malaysia.

出版信息

Sci Rep. 2025 Sep 5;15(1):32376. doi: 10.1038/s41598-025-16255-3.

DOI:10.1038/s41598-025-16255-3

PMID:40913062

原文链接:https://pmc.ncbi.nlm.nih.gov/articles/PMC12413442/

Abstract

This article demonstrates maximum power point tracking (MPPT) using a DC-DC boost converter. It introduces an intelligent control technique with fuzzy-based pattern search (PS) optimization for the MPPT controller, enhancing energy conversion efficiency. The fuzzy-PS approach is further refined with PA optimization. A comprehensive performance evaluation compares it with various optimization algorithms. The controller is tested under changes in irradiance and temperature, showing its performance against the Perturb and Observe (P&O) algorithm. The fuzzy controller is optimized to provide the best membership functions (MFs) using PS optimization, particle swarm optimization (PSO), and genetic algorithm (GA), with root mean square error (RMSE) as the objective function. PS optimization outperforms other algorithms. The fuzzy-PS optimization achieves the lowest RMSE of 0.6861 after 100 iterations, while fuzzy-GA and fuzzy-PSO reach RMSEs of 1.257 and 0.9454, respectively. The proposed fuzzy-PS MPPT controller effectively adapts to irradiance and temperature variations, achieving maximum power outputs up to 74.48 kW and Comparative evaluations revealed an average MPPT efficiency of 99.7%, demonstrating superior tracking performance compared to the P&O algorithm.

摘要

本文展示了使用DC-DC升压转换器的最大功率点跟踪（MPPT）。它介绍了一种用于MPPT控制器的基于模糊模式搜索（PS）优化的智能控制技术，提高了能量转换效率。模糊PS方法通过PA优化进一步完善。进行了全面的性能评估，并将其与各种优化算法进行比较。在光照和温度变化的情况下对控制器进行了测试，展示了其相对于扰动观察（P&O）算法的性能。使用PS优化、粒子群优化（PSO）和遗传算法（GA），以均方根误差（RMSE）为目标函数，对模糊控制器进行优化以提供最佳隶属函数（MF）。PS优化优于其他算法。模糊PS优化在100次迭代后实现了最低RMSE为0.6861，而模糊GA和模糊PSO分别达到RMSE为1.257和0.9454。所提出的模糊PS MPPT控制器有效地适应光照和温度变化，实现了高达74.48kW的最大功率输出，比较评估显示平均MPPT效率为99.7%，表明与P&O算法相比具有卓越的跟踪性能。