Novel fast and high accuracy maximum power point tracking method for hybrid photovoltaic/fuel cell energy conversion systems

被引:33
作者
Fathabadi, Hassan [1 ]
机构
[1] Natl Tech Univ Athens, Sch Elect & Comp Engn, Athens, Greece
关键词
Maximum power point tracking; Hybrid system; Photovoltaic; Fuel cell; MICROBIAL FUEL-CELLS; EXTREMUM SEEKING CONTROL; PARTIALLY SHADED CONDITIONS; VEHICLE CHARGING STATION; CIRCUIT FAULTS DETECTION; MPPT METHOD; PV SYSTEMS; TRANSMISSION-LINES; SHADING CONDITIONS; ANN APPROACH;
D O I
10.1016/j.renene.2017.01.028
中图分类号
X [环境科学、安全科学];
学科分类号
08 ; 0830 ;
摘要
Currently, two maximum power point tracking (MPPT) units are used in hybrid photovoltaic (PV)/fuel cell (FC) systems, one for the PV subsystem and the other one for the FC stack, which significantly complicates the system implementation, and increases cost. This paper addresses this problem by presenting a novel fast and highly accurate unified MPPT technique for extracting maximum output power from hybrid PV/FC energy conversion systems. It is the only unified MPPT technique reported in the literature that uses a single algorithm to concurrently track the maximum power points (MPPs) of both PV module and FC stack used in a hybrid PV/FC system. A hybrid PV/FC energy conversion system has been built to evaluate the performance of the method. It is experimentally verified that the proposed MPPT method performs a very fast and highly accurate MPPT process, so that, the MPPT efficiency in the PV and FC subsystems is more than 99.60% and 99.41%, respectively, along with a very short convergence time of at most 12 ms and 33 s, respectively. Comparison between the MPPT method presented in this work and the state-of-the-art MPPT methods has been also performed that explicitly demonstrates the method has the highest MPPT efficiencies (99.60%, and 99.41%) along with the shortest convergence time (12 ms) compared to the state-of-the-art MPPT methods, while it concurrently performs two tasks (tracking two MPPs) but others perform only one task (tracking one MPP). (C) 2017 Elsevier Ltd. All rights reserved.
引用
收藏
页码:232 / 242
页数:11
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