Dhimish, Mahmoud, Holmes, Violeta, Mehrdadi, Bruce et al. (1 more author) (2017) Multi-layer photovoltaic fault detection algorithm. IET High Voltage. pp. 244-252.
Abstract
This study proposes a fault detection algorithm based on the analysis of the theoretical curves which describe the behaviour of an existing grid-connected photovoltaic (GCPV) system. For a given set of working conditions, a number of attributes such as voltage ratio (VR) and power ratio (PR) are simulated using virtual instrumentation LabVIEW software. Furthermore, a third-order polynomial function is used to generate two detection limits (high and low limits) for the VR and PR ratios. The high and low detection limits are compared with real-time long-term data measurements from a 1.1 kWp GCPV system installed at the University of Huddersfield, United Kingdom. Furthermore, samples that lie out of the detecting limits are processed by a fuzzy logic classification system which consists of two inputs (VR and PR) and one output membership function. The obtained results show that the fault detection algorithm accurately detects different faults occurring in the PV system. The maximum detection accuracy (DA) of the proposed algorithm before considering the fuzzy logic system is equal to 95.27%; however, the fault DA is increased up to a minimum value of 98.8% after considering the fuzzy logic system.
Metadata
Item Type: | Article |
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Authors/Creators: |
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Copyright, Publisher and Additional Information: | © IET 2017. This is an author-produced version of the published paper. Uploaded in accordance with the publisher’s self-archiving policy. Further copying may not be permitted; contact the publisher for details |
Dates: |
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Institution: | The University of York |
Academic Units: | The University of York > Faculty of Sciences (York) > Electronic Engineering (York) |
Depositing User: | Pure (York) |
Date Deposited: | 01 Sep 2021 14:20 |
Last Modified: | 16 Oct 2024 17:48 |
Published Version: | https://doi.org/10.1049/hve.2017.0044 |
Status: | Published |
Refereed: | Yes |
Identification Number: | 10.1049/hve.2017.0044 |
Related URLs: | |
Open Archives Initiative ID (OAI ID): | oai:eprints.whiterose.ac.uk:177680 |