Diagnostic module for series-connected photovoltaic panels

Martin Garaj; Kelvin Yiwen Hong; Henry Shu Hung Chung; Alan Wai lun Lo; Huai Wang

doi:10.1016/j.solener.2019.12.019

Diagnostic module for series-connected photovoltaic panels

Martin Garaj^*, Kelvin Yiwen Hong, Henry Shu Hung Chung, Alan Wai lun Lo, Huai Wang

^*Corresponding author for this work

Research output: Contribution to journal › Journal article › Research › peer-review

3 Citations (Scopus)

Abstract

An online diagnostic module for condition monitoring of two series-connected photovoltaic panels is presented. The technique is based on firstly perturbing the terminal voltages and currents of the panels with a switched-inductor circuit, which can also be used for differential power processing, to obtain the large-signal dynamic current-voltage characteristics of the panels. An evolutionary algorithm is used to estimate the intrinsic parameters of the panels from the time series of the sampled panel current and voltage. The conditions of the panels are monitored by observing the long-term changes in the extracted intrinsic parameters. Prototype data acquisition module for studying the conditions of solar panels of different technologies (amorphous and crystalline silicon) with different degrees of damage has been built and evaluated. Results reveal that the estimated intrinsic parameters from large-signal dynamic characteristic correlate with the observed health status of the tested panels. Theoretical predictions are favorably compared with experimental measurements.

Original language	English
Journal	Solar Energy
Volume	196
Pages (from-to)	243-259
Number of pages	17
ISSN	0038-092X
DOIs	https://doi.org/10.1016/j.solener.2019.12.019
Publication status	Published - Jan 2020

Bibliographical note

Funding Information:
The work was supported by a grant from the Innovation Fund Denmark through the project APETT with no.: 6154-00010B .

Publisher Copyright:
© 2019 International Solar Energy Society

Copyright:
Copyright 2019 Elsevier B.V., All rights reserved.

Keywords

Evolutionary computation
Fault diagnosis
Photovoltaic panels
Photovoltaic systems

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10.1016/j.solener.2019.12.019

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@article{f6f0763c1ad94fec903fe6beb1db46d5,

title = "Diagnostic module for series-connected photovoltaic panels",

abstract = "An online diagnostic module for condition monitoring of two series-connected photovoltaic panels is presented. The technique is based on firstly perturbing the terminal voltages and currents of the panels with a switched-inductor circuit, which can also be used for differential power processing, to obtain the large-signal dynamic current-voltage characteristics of the panels. An evolutionary algorithm is used to estimate the intrinsic parameters of the panels from the time series of the sampled panel current and voltage. The conditions of the panels are monitored by observing the long-term changes in the extracted intrinsic parameters. Prototype data acquisition module for studying the conditions of solar panels of different technologies (amorphous and crystalline silicon) with different degrees of damage has been built and evaluated. Results reveal that the estimated intrinsic parameters from large-signal dynamic characteristic correlate with the observed health status of the tested panels. Theoretical predictions are favorably compared with experimental measurements.",

keywords = "Evolutionary computation, Fault diagnosis, Photovoltaic panels, Photovoltaic systems",

author = "Martin Garaj and Hong, {Kelvin Yiwen} and Chung, {Henry Shu Hung} and Lo, {Alan Wai lun} and Huai Wang",

note = "Funding Information: The work was supported by a grant from the Innovation Fund Denmark through the project APETT with no.: 6154-00010B . Publisher Copyright: {\textcopyright} 2019 International Solar Energy Society Copyright: Copyright 2019 Elsevier B.V., All rights reserved.",

year = "2020",

month = jan,

doi = "10.1016/j.solener.2019.12.019",

language = "English",

volume = "196",

pages = "243--259",

journal = "Solar Energy",

issn = "0038-092X",

publisher = "Pergamon Press",

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TY - JOUR

T1 - Diagnostic module for series-connected photovoltaic panels

AU - Garaj, Martin

AU - Hong, Kelvin Yiwen

AU - Chung, Henry Shu Hung

AU - Lo, Alan Wai lun

AU - Wang, Huai

N1 - Funding Information: The work was supported by a grant from the Innovation Fund Denmark through the project APETT with no.: 6154-00010B . Publisher Copyright: © 2019 International Solar Energy Society Copyright: Copyright 2019 Elsevier B.V., All rights reserved.

PY - 2020/1

Y1 - 2020/1

N2 - An online diagnostic module for condition monitoring of two series-connected photovoltaic panels is presented. The technique is based on firstly perturbing the terminal voltages and currents of the panels with a switched-inductor circuit, which can also be used for differential power processing, to obtain the large-signal dynamic current-voltage characteristics of the panels. An evolutionary algorithm is used to estimate the intrinsic parameters of the panels from the time series of the sampled panel current and voltage. The conditions of the panels are monitored by observing the long-term changes in the extracted intrinsic parameters. Prototype data acquisition module for studying the conditions of solar panels of different technologies (amorphous and crystalline silicon) with different degrees of damage has been built and evaluated. Results reveal that the estimated intrinsic parameters from large-signal dynamic characteristic correlate with the observed health status of the tested panels. Theoretical predictions are favorably compared with experimental measurements.

AB - An online diagnostic module for condition monitoring of two series-connected photovoltaic panels is presented. The technique is based on firstly perturbing the terminal voltages and currents of the panels with a switched-inductor circuit, which can also be used for differential power processing, to obtain the large-signal dynamic current-voltage characteristics of the panels. An evolutionary algorithm is used to estimate the intrinsic parameters of the panels from the time series of the sampled panel current and voltage. The conditions of the panels are monitored by observing the long-term changes in the extracted intrinsic parameters. Prototype data acquisition module for studying the conditions of solar panels of different technologies (amorphous and crystalline silicon) with different degrees of damage has been built and evaluated. Results reveal that the estimated intrinsic parameters from large-signal dynamic characteristic correlate with the observed health status of the tested panels. Theoretical predictions are favorably compared with experimental measurements.

KW - Evolutionary computation

KW - Fault diagnosis

KW - Photovoltaic panels

KW - Photovoltaic systems

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U2 - 10.1016/j.solener.2019.12.019

DO - 10.1016/j.solener.2019.12.019

M3 - Journal article

AN - SCOPUS:85076414444

SN - 0038-092X

VL - 196

SP - 243

EP - 259

JO - Solar Energy

JF - Solar Energy

ER -

Diagnostic module for series-connected photovoltaic panels

Abstract

Bibliographical note

Keywords

Access to Document

AUB Link

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Fingerprint

APETT: Advanced Power Electronic Technology and Tools

Cite this

Diagnostic module for series-connected photovoltaic panels

Abstract

Bibliographical note

Keywords

Access to Document

AUB Link

Other files and links

Fingerprint

Projects

APETT: Advanced Power Electronic Technology and Tools

Cite this