Simultaneous fault detection algorithm for grid-connected photovoltaic plants

Mahmoud Dhimish, Violeta Holmes, Bruce Mehrdadi, Mark Dales

Research output: Contribution to journalArticle

16 Citations (Scopus)

Abstract

In this work, the authors present a new algorithm for detecting faults in grid-connected photovoltaic (GCPV) plant. There are few instances of statistical tools being deployed in the analysis of photovoltaic (PV) measured data. The main focus of this study is, therefore, to outline a PV fault detection algorithm that can diagnose faults on the DC side of the examined GCPV system based on the t-test statistical analysis method. For a given set of operational conditions, solar irradiance and module temperature, a number of attributes such as voltage and power ratio of the PV strings are measured using virtual instrumentation (VI) LabVIEW software. The results obtained indicate that the fault detection algorithm can detect accurately different types of faults such as, faulty PV module, faulty PV String, faulty Bypass diode and faulty maximum power point tracking unit. The proposed PV fault detection algorithm has been validated using 1.98 kWp PV plant installed at the University of Huddersfield, UK.
LanguageEnglish
Pages1565-1575
Number of pages11
JournalIET Renewable Power Generation
Volume11
Issue number12
Early online date27 Jul 2017
DOIs
Publication statusPublished - 18 Oct 2017

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Fault detection
Failure analysis
Statistical methods
Diodes
Electric potential
Temperature

Cite this

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title = "Simultaneous fault detection algorithm for grid-connected photovoltaic plants",
abstract = "In this work, the authors present a new algorithm for detecting faults in grid-connected photovoltaic (GCPV) plant. There are few instances of statistical tools being deployed in the analysis of photovoltaic (PV) measured data. The main focus of this study is, therefore, to outline a PV fault detection algorithm that can diagnose faults on the DC side of the examined GCPV system based on the t-test statistical analysis method. For a given set of operational conditions, solar irradiance and module temperature, a number of attributes such as voltage and power ratio of the PV strings are measured using virtual instrumentation (VI) LabVIEW software. The results obtained indicate that the fault detection algorithm can detect accurately different types of faults such as, faulty PV module, faulty PV String, faulty Bypass diode and faulty maximum power point tracking unit. The proposed PV fault detection algorithm has been validated using 1.98 kWp PV plant installed at the University of Huddersfield, UK.",
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Simultaneous fault detection algorithm for grid-connected photovoltaic plants. / Dhimish, Mahmoud; Holmes, Violeta; Mehrdadi, Bruce; Dales, Mark.

In: IET Renewable Power Generation, Vol. 11, No. 12, 18.10.2017, p. 1565-1575.

Research output: Contribution to journalArticle

TY - JOUR

T1 - Simultaneous fault detection algorithm for grid-connected photovoltaic plants

AU - Dhimish, Mahmoud

AU - Holmes, Violeta

AU - Mehrdadi, Bruce

AU - Dales, Mark

PY - 2017/10/18

Y1 - 2017/10/18

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AB - In this work, the authors present a new algorithm for detecting faults in grid-connected photovoltaic (GCPV) plant. There are few instances of statistical tools being deployed in the analysis of photovoltaic (PV) measured data. The main focus of this study is, therefore, to outline a PV fault detection algorithm that can diagnose faults on the DC side of the examined GCPV system based on the t-test statistical analysis method. For a given set of operational conditions, solar irradiance and module temperature, a number of attributes such as voltage and power ratio of the PV strings are measured using virtual instrumentation (VI) LabVIEW software. The results obtained indicate that the fault detection algorithm can detect accurately different types of faults such as, faulty PV module, faulty PV String, faulty Bypass diode and faulty maximum power point tracking unit. The proposed PV fault detection algorithm has been validated using 1.98 kWp PV plant installed at the University of Huddersfield, UK.

KW - Photovoltaic power systems

KW - Fault Diagnosis

KW - statistical analysis

KW - power grids

KW - Virtual instrumentation

KW - maximum power point trackers

KW - power engineering computing

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