1D-CNN based real-time fault detection system for power asset diagnostics
- Submitting institution
-
Glasgow Caledonian University
- Unit of assessment
- 11 - Computer Science and Informatics
- Output identifier
- 42309072
- Type
- D - Journal article
- DOI
-
10.1049/iet-gtd.2020.0773
- Title of journal
- IET Generation, Transmission & Distribution
- Article number
- -
- First page
- 5766
- Volume
- 14
- Issue
- 24
- ISSN
- 1751-8687
- Open access status
- Compliant
- Month of publication
- September
- Year of publication
- 2020
- URL
-
-
- Supplementary information
-
-
- Request cross-referral to
- -
- Output has been delayed by COVID-19
- No
- COVID-19 affected output statement
- -
- Forensic science
- No
- Criminology
- No
- Interdisciplinary
- No
- Number of additional authors
-
4
- Research group(s)
-
-
- Citation count
- 0
- Proposed double-weighted
- No
- Reserve for an output with double weighting
- No
- Additional information
- An end-to-end fault classification algorithm based on real-world Electromagnetic Interference (EMI) time-resolved signals was developed. The proposed algorithm exploits the raw measured time-resolved signals into a 1D-CNN which eliminates the need for engineered feature extraction and reduces computation time. The optimum algorithm, in terms of computation and performance, was implemented into the Doble Portland instrument, for fault detection, monitoring and analysis. A combination of tests at a major UK power station and in high-voltage laboratory have contributed to a successful domain expert labelled dataset from which the algorithm has been trained and optimised to match experts’ analytical level.
- Author contribution statement
- -
- Non-English
- No
- English abstract
- -