LAPSE:2023.29276
Published Article
LAPSE:2023.29276
A Fault Diagnosis Mechanism with Power Generation Improvement for a Photovoltaic Module Array
Kuei-Hsiang Chao, Pei-Lun Lai
April 13, 2023
This paper aims to develop an online diagnostic mechanism, doubling as a maximum power point tracking scheme, for a photovoltaic (PV) module array. In case of malfunction or shadow event occurring to a PV module, the presented diagnostic mechanism is enabled, automatically and immediately, to reconfigure a PV module array for maximum output power operation under arbitrary working conditions. Meanwhile, the malfunctioning or shaded PV module can be located instantly by this diagnostic mechanism according to the array configuration, and a PV module replacement process is made more efficient than ever before for the maintenance crew. In this manner, the intended maximum output power operation can be resumed as soon as possible in consideration of a minimum business loss. Using a particle swarm optimization (PSO)-based algorithm, the PV module array is reconfigured by means of switch manipulations between modules, such that a load is supplied with the maximum amount of output power. For compactness, the PSO-based online diagnostic algorithm is implemented herein using a TMS320F2808 digital signal processor (DSP) and is experimentally validated as successful to identify a malfunctioning PV module at the end of this work.
Keywords
digital signal processor, maximum power point tracking, online diagnostic mechanism, Particle Swarm Optimization, photovoltaic module array
Suggested Citation
Chao KH, Lai PL. A Fault Diagnosis Mechanism with Power Generation Improvement for a Photovoltaic Module Array. (2023). LAPSE:2023.29276
Author Affiliations
Chao KH: Department of Electrical Engineering, National Chin-Yi University of Technology, Taichung 41170, Taiwan [ORCID]
Lai PL: Department of Electrical Engineering, National Chin-Yi University of Technology, Taichung 41170, Taiwan
Journal Name
Energies
Volume
14
Issue
3
First Page
598
Year
2021
Publication Date
2021-01-25
Published Version
ISSN
1996-1073
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Original Submission
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PII: en14030598, Publication Type: Journal Article
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doi:10.3390/en14030598
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Apr 13, 2023
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