Classification of EMI discharge sources using time–frequency features and multi-class support vector machine
- Submitting institution
-
Glasgow Caledonian University
- Unit of assessment
- 12 - Engineering
- Output identifier
- 33575221
- Type
- D - Journal article
- DOI
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10.1016/j.epsr.2018.06.016
- Title of journal
- Electric Power Systems Research
- Article number
- -
- First page
- 261
- Volume
- 163
- Issue
- Part A
- ISSN
- 0378-7796
- Open access status
- Compliant
- Month of publication
- July
- Year of publication
- 2018
- 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
- Yes
- Number of additional authors
-
5
- Research group(s)
-
-
- Proposed double-weighted
- No
- Reserve for an output with double weighting
- No
- Additional information
- For the first time, this work transferred expert’s knowledge on EMI faults to an intelligent system through signal processing analysis and machine learning based algorithm. The high classification accuracy in this work demonstrated that it is feasible in the field to identify fault patterns from the EMI signals and classify Partial Discharge among other insulation faults. The developed algorithms captured and learned expert knowledge from advanced practitioners.
- Author contribution statement
- -
- Non-English
- No
- English abstract
- -