Photovoltaic Panel Health Diagnostic System for Solar Power Plants

Martin Garaj; Kelvin Yiwen Hong; Henry Shu-Hung Chung; Jianfeng Zhou; Alan Wai-Lun Lo

doi:10.1109/APEC.2019.8721839

Photovoltaic Panel Health Diagnostic System for Solar Power Plants

Martin Garaj, Kelvin Yiwen Hong, Henry Shu-Hung Chung, Jianfeng Zhou, Alan Wai-Lun Lo

Department of Electrical Engineering

Research output: Chapters, Conference Papers, Creative and Literary Works › RGC 32 - Refereed conference paper (with host publication) › peer-review

15 Citations (Scopus)

Abstract

A photovoltaic (PV) health diagnostic system for solar power systems is presented. The system consists of two levels of embedded platforms, including the Data Acquisition Module (DAM) and the Control Module (CM). Each DAM is connected to two series-connected PV panels under test. When testing the PV panels, it will inject large-signal disturbances into their panel voltages. Then, the terminal voltage and current of each panel are sampled and, thus, the dynamic current-voltage characteristics of the PV panel are obtained. The CM has a Real-coded Jumping Gene Genetic Algorithm (RJGGA) programmed and a dedicated Field Programmable Gate Array (FPGA) accelerator designed to evaluate objective functions. Its function is to extract the intrinsic parameters of the panels with the dynamic current-voltage characteristics. Panel degradation can thus be observed with the variation of the estimated intrinsic parameters. Prototypes designed for diagnosing four 80W PV panels have been built and evaluated on panels with different degradation levels.

Original language	English
Title of host publication	APEC 2019 - Thirty-Fourth Annual IEEE Applied Power Electronics Conference and Exposition
Publisher	IEEE
Pages	1078-1083
ISBN (Print)	9781538683309
DOIs	https://doi.org/10.1109/APEC.2019.8721839
Publication status	Published - Mar 2019
Event	34th Annual IEEE Applied Power Electronics Conference and Exposition (APEC 2019) - Anaheim Convention Center, Anaheim, United States Duration: 17 Mar 2019 → 21 Mar 2019

Publication series

Name	Conference Proceedings - IEEE Applied Power Electronics Conference and Exposition - APEC
Volume	2019-March
ISSN (Electronic)	2470-6647

Conference

Conference	34th Annual IEEE Applied Power Electronics Conference and Exposition (APEC 2019)
Country/Territory	United States
City	Anaheim
Period	17/03/19 → 21/03/19

Research Keywords

Diagnostics
Distributed energy sources
Photovoltaic systems
Renewable energy
Solar panels

UN SDGs

This output contributes to the following UN Sustainable Development Goals (SDGs)

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10.1109/APEC.2019.8721839

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Garaj, M., Hong, K. Y., Shu-Hung Chung, H., Zhou, J., & Lo, A. W.-L. (2019). Photovoltaic Panel Health Diagnostic System for Solar Power Plants. In APEC 2019 - Thirty-Fourth Annual IEEE Applied Power Electronics Conference and Exposition (pp. 1078-1083). Article 8721839 (Conference Proceedings - IEEE Applied Power Electronics Conference and Exposition - APEC; Vol. 2019-March). IEEE. https://doi.org/10.1109/APEC.2019.8721839

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title = "Photovoltaic Panel Health Diagnostic System for Solar Power Plants",

abstract = "A photovoltaic (PV) health diagnostic system for solar power systems is presented. The system consists of two levels of embedded platforms, including the Data Acquisition Module (DAM) and the Control Module (CM). Each DAM is connected to two series-connected PV panels under test. When testing the PV panels, it will inject large-signal disturbances into their panel voltages. Then, the terminal voltage and current of each panel are sampled and, thus, the dynamic current-voltage characteristics of the PV panel are obtained. The CM has a Real-coded Jumping Gene Genetic Algorithm (RJGGA) programmed and a dedicated Field Programmable Gate Array (FPGA) accelerator designed to evaluate objective functions. Its function is to extract the intrinsic parameters of the panels with the dynamic current-voltage characteristics. Panel degradation can thus be observed with the variation of the estimated intrinsic parameters. Prototypes designed for diagnosing four 80W PV panels have been built and evaluated on panels with different degradation levels.",

keywords = "Diagnostics, Distributed energy sources, Photovoltaic systems, Renewable energy, Solar panels",

author = "Martin Garaj and Hong, {Kelvin Yiwen} and {Shu-Hung Chung}, Henry and Jianfeng Zhou and Lo, {Alan Wai-Lun}",

year = "2019",

month = mar,

doi = "10.1109/APEC.2019.8721839",

language = "English",

isbn = "9781538683309",

series = "Conference Proceedings - IEEE Applied Power Electronics Conference and Exposition - APEC",

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booktitle = "APEC 2019 - Thirty-Fourth Annual IEEE Applied Power Electronics Conference and Exposition",

address = "United States",

note = "34th Annual IEEE Applied Power Electronics Conference and Exposition (APEC 2019)<br/> ; Conference date: 17-03-2019 Through 21-03-2019",

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Garaj, M, Hong, KY, Shu-Hung Chung, H , Zhou, J & Lo, AW-L 2019, Photovoltaic Panel Health Diagnostic System for Solar Power Plants. in APEC 2019 - Thirty-Fourth Annual IEEE Applied Power Electronics Conference and Exposition., 8721839, Conference Proceedings - IEEE Applied Power Electronics Conference and Exposition - APEC, vol. 2019-March, IEEE, pp. 1078-1083, 34th Annual IEEE Applied Power Electronics Conference and Exposition (APEC 2019)
, Anaheim, California, United States, 17/03/19. https://doi.org/10.1109/APEC.2019.8721839

Photovoltaic Panel Health Diagnostic System for Solar Power Plants. / Garaj, Martin; Hong, Kelvin Yiwen; Shu-Hung Chung, Henry et al.
APEC 2019 - Thirty-Fourth Annual IEEE Applied Power Electronics Conference and Exposition. IEEE, 2019. p. 1078-1083 8721839 (Conference Proceedings - IEEE Applied Power Electronics Conference and Exposition - APEC; Vol. 2019-March).

Research output: Chapters, Conference Papers, Creative and Literary Works › RGC 32 - Refereed conference paper (with host publication) › peer-review

TY - GEN

T1 - Photovoltaic Panel Health Diagnostic System for Solar Power Plants

AU - Garaj, Martin

AU - Hong, Kelvin Yiwen

AU - Shu-Hung Chung, Henry

AU - Zhou, Jianfeng

AU - Lo, Alan Wai-Lun

PY - 2019/3

Y1 - 2019/3

N2 - A photovoltaic (PV) health diagnostic system for solar power systems is presented. The system consists of two levels of embedded platforms, including the Data Acquisition Module (DAM) and the Control Module (CM). Each DAM is connected to two series-connected PV panels under test. When testing the PV panels, it will inject large-signal disturbances into their panel voltages. Then, the terminal voltage and current of each panel are sampled and, thus, the dynamic current-voltage characteristics of the PV panel are obtained. The CM has a Real-coded Jumping Gene Genetic Algorithm (RJGGA) programmed and a dedicated Field Programmable Gate Array (FPGA) accelerator designed to evaluate objective functions. Its function is to extract the intrinsic parameters of the panels with the dynamic current-voltage characteristics. Panel degradation can thus be observed with the variation of the estimated intrinsic parameters. Prototypes designed for diagnosing four 80W PV panels have been built and evaluated on panels with different degradation levels.

AB - A photovoltaic (PV) health diagnostic system for solar power systems is presented. The system consists of two levels of embedded platforms, including the Data Acquisition Module (DAM) and the Control Module (CM). Each DAM is connected to two series-connected PV panels under test. When testing the PV panels, it will inject large-signal disturbances into their panel voltages. Then, the terminal voltage and current of each panel are sampled and, thus, the dynamic current-voltage characteristics of the PV panel are obtained. The CM has a Real-coded Jumping Gene Genetic Algorithm (RJGGA) programmed and a dedicated Field Programmable Gate Array (FPGA) accelerator designed to evaluate objective functions. Its function is to extract the intrinsic parameters of the panels with the dynamic current-voltage characteristics. Panel degradation can thus be observed with the variation of the estimated intrinsic parameters. Prototypes designed for diagnosing four 80W PV panels have been built and evaluated on panels with different degradation levels.

KW - Diagnostics

KW - Distributed energy sources

KW - Photovoltaic systems

KW - Renewable energy

KW - Solar panels

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U2 - 10.1109/APEC.2019.8721839

DO - 10.1109/APEC.2019.8721839

M3 - RGC 32 - Refereed conference paper (with host publication)

SN - 9781538683309

T3 - Conference Proceedings - IEEE Applied Power Electronics Conference and Exposition - APEC

SP - 1078

EP - 1083

BT - APEC 2019 - Thirty-Fourth Annual IEEE Applied Power Electronics Conference and Exposition

PB - IEEE

T2 - 34th Annual IEEE Applied Power Electronics Conference and Exposition (APEC 2019)<br/>

Y2 - 17 March 2019 through 21 March 2019

ER -

Garaj M, Hong KY, Shu-Hung Chung H , Zhou J, Lo AWL. Photovoltaic Panel Health Diagnostic System for Solar Power Plants. In APEC 2019 - Thirty-Fourth Annual IEEE Applied Power Electronics Conference and Exposition. IEEE. 2019. p. 1078-1083. 8721839. (Conference Proceedings - IEEE Applied Power Electronics Conference and Exposition - APEC). doi: 10.1109/APEC.2019.8721839

Photovoltaic Panel Health Diagnostic System for Solar Power Plants

Abstract

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Research Keywords

UN SDGs

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