Publication2025
Ensemble Learning for Enhanced Fault Classification in Smart Distribution System
The record
- Type
- Publication
- Date
- 1 January 2025
- Level
- National
- Journal
- 3rd IEEE International Conference on Industrial Electronics Developments and Applications Icidea 2025
- Published
- 1 January 2025
- DOI
- 10.1109/icidea64800.2025.10962856
- Scopus ID
- 105004551006
- Authors
- 4
- Open access
- No
- Citations
- 2
- International co-authorship
- No
- Co-author countries
- IN
Classification
Every label below carries the reasoning it was assigned on, the confidence band it was assigned in, and what produced it. The rubric is published.
UN Sustainable Development Goals
7.2 Substantially increase the share of renewable energy in the global energy mix
Compares machine learning ensemble methods achieving 99.46% accuracy for fault classification in transmission line networks.
- High confidence
- The subject matter is a strong and direct inference from the record type, the funding or organising body, or the programme context, even where the title alone is thin. Example: an ICMR-funded project against SDG 3, on the strength of the agency.
- Judged
- Judged against the published classification rubric, under the rubric and prompt versions recorded with the run that produced it.
- Produced by
- claude-haiku-4-5-20251001, under kortex-publication-v1
9.5 Enhance scientific research, upgrade technological capabilities, and increase researchers and R&D spending
Evaluates traditional machine learning approaches as viable alternatives to deep learning for power system diagnostics.
- Moderate confidence
- The alignment is plausible and defensible but rests on an assumption a reasonable reviewer could decline. Published, and surfaced in the review queue for a human to confirm or override.
- Produced by
- claude-haiku-4-5-20251001, under kortex-publication-v1
Viksit Bharat @2047
vb-2.5 Green economy, energy transition and climate resilience
Smart distribution system fault detection for reliable energy delivery and reduced grid downtime.
- Produced by
- claude-haiku-4-5-20251001, under kortex-publication-v1
Each label above carries the instrument and the version that produced it. How each classification is reached.
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- Source rows resolved into this record
- 1
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Citing this record
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KIIT Deemed to be University. “Ensemble Learning for Enhanced Fault Classification in Smart Distribution System”. Kortex.