Publication2024
Improved fault detection and classification in PV arrays using stockwell transform and data mining techniques
The record
- Type
- Publication
- Date
- 1 September 2024
- Level
- National
- Journal
- Results in Engineering
- Volume
- 23
- Published
- 1 September 2024
- DOI
- 10.1016/j.rineng.2024.102808
- Scopus ID
- 85202940999
- ISSN
- 25901230
- Authors
- 4
- Open access
- Yes
- Citations
- 42
- 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
Fault detection in photovoltaic systems using machine learning directly supports renewable energy reliability and efficiency management.
- 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
Data mining and signal processing techniques represent research innovation in computational methods.
- 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
PV system reliability improvement directly supports renewable energy transition and green economy goals.
- 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. “Improved fault detection and classification in PV arrays using stockwell transform and data mining techniques”. Kortex.