Predictive Maximum Power Point Tracking for Proton Exchange Membrane Fuel Cell System

被引：14

作者：

Fam, Jye Yun ^{[1
]}

Wong, Shen Yuong ^{[2
]}

Basri, Hazrul Bin Mohamed ^{[1
]}

Abdullah, Mohammad Omar ^{[1
]}

Lias, Kasumawati Binti ^{[1
]}

Mekhilef, Saad ^{[3
]}

机构：

[1] Univ Malaysia Sarawak, Fac Engn, Sarawak 94300, Malaysia

[2] Xiamen Univ Malaysia, Dept Elect & Elect Engn, Sepang 43900, Malaysia

[3] Swinburne Univ Technol, Sch Sci Comp & Engn Technol, Melbourne, Vic 3122, Australia

来源：

IEEE ACCESS | 2021年 / 9卷 / 09期

关键词：

Maximum power point trackers; Fuel cells; Prediction algorithms; Power generation; Convergence; Stability analysis; Complexity theory; MATLAB; FC; PEMFC; DC-DC boost converter; MPPT; SLIDING MODE CONTROL; BOOST CONVERTER;

D O I：

10.1109/ACCESS.2021.3129849

中图分类号：

TP [自动化技术、计算机技术];

学科分类号：

0812 ;

摘要：

This project aims to design a predictive maximum power point tracking (MPPT) for a proton exchange membrane fuel cell system (PEMFC). This predictive MPPT includes the predictive control algorithm of a DC-DC boost converter in the fully functional mathematical modeling of the PEMFC system. The DC-DC boost converter is controlled by the MPPT algorithm and regulates the voltage of the PEMFC to extract the maximum output power. All simulations were performed using MATLAB software to show the power characteristics extracted from the PEMFC system. As a result, the newly designed predictive MPPT algorithm has a fast-tracking of maximum power point (MPP) for different fuel cell (FC) parameters. It is confirmed that the proposed MPPT technique exhibits fast tracking of the MPP locus, outstanding accuracy, and robustness with respect to environmental changes. Furthermore, its MPP tracking time is at least five times faster than that of the particle swarm optimizer with the proportional-integral-derivative controller method.

引用

页码：157384 / 157397

页数：14

共 30 条

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