Publication2018
An Improved Method Using Artificial Neural Network for Fault Detection and Fault Pole Identification in Voltage Source Converter-Based High-Voltage Direct Current Transmission Lines
The record
- Type
- Publication
- Date
- 1 August 2018
- Level
- National
- Journal
- Arabian Journal for Science and Engineering
- Volume
- 43
- Issue
- 8
- Pages
- 4005-4018
- Published
- 1 August 2018
- DOI
- 10.1007/s13369-017-2791-9
- Scopus ID
- 85049442268
- ISSN
- 2193567X
- Authors
- 3
- Open access
- No
- Citations
- 21
- 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 Ensure access to affordable, reliable, sustainable and modern energy for all
Back-propagation neural network method detects faults and identifies faulty poles in VSC-HVDC transmission lines with 99.9% reach setting accuracy.
- 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
Artificial neural network methodology innovation for power transmission system fault detection and protection.
- 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
HVDC transmission technology reliability improving secure and efficient energy infrastructure.
- 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. “An Improved Method Using Artificial Neural Network for Fault Detection and Fault Pole Identification in Voltage Source Converter-Based High-Voltage Direct Current Transmission Lines”. Kortex.