Simultaneous fault detection algorithm for grid-connected photovoltaic plants

Mahmoud Dhimish, Violeta Holmes, Bruce Mehrdadi, Mark Dales

Research output: Contribution to journalArticle

31 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.
Original languageEnglish
Pages (from-to)1565-1575
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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