Comparing Mamdani Sugeno Fuzzy Logic and RBF ANN Network for PV Fault Detection
- Submitting institution
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The University of Huddersfield
- Unit of assessment
- 12 - Engineering
- Output identifier
- 55
- Type
- D - Journal article
- DOI
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10.1016/j.renene.2017.10.066
- Title of journal
- Renewable Energy
- Article number
- -
- First page
- 257
- Volume
- 117
- Issue
- -
- ISSN
- 0960-1481
- Open access status
- Compliant
- Month of publication
- October
- Year of publication
- 2017
- URL
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- Supplementary information
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- Request cross-referral to
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- Output has been delayed by COVID-19
- No
- COVID-19 affected output statement
- -
- Forensic science
- No
- Criminology
- No
- Interdisciplinary
- No
- Number of additional authors
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3
- Research group(s)
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-
- Proposed double-weighted
- No
- Reserve for an output with double weighting
- No
- Additional information
- We developed a novel PV fault detection algorithm capable of detecting complex faults in PV installations, using artificial intelligence-based techniques. The algorithm was deployed in monitoring/management of PV systems and accurately detected faulty modules, and attracted research funding from industry. This research was published in October 2017 by Renewable Energy (journal rank: Q1 based on Scimago). The algorithm has been restated by leading international institutions such as University of Tsukuba (Japan), Fuzhou University (China), University of Oslo (Norway), Université Paris-Saclay, CentraleSupélec, CNRS, Sorbonne Université, GeePs (France) and Arizona State University (USA). Based on Google Scholar, the article attracted 90 citations.
- Author contribution statement
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- Non-English
- No
- English abstract
- -