Diagnostic Accuracy and Technical Considerations for MV Cable Field Partial Discharge Measurements
Bibliographic record
Abstract
Partial discharge (PD) field-testing has been used widely used to reveal ‘dry electrical’ type defects in extruded medium voltage (MV) cable systems, covering varying application contexts, system criticality levels and asset management objectives. In terms of PD measurement systems intended for field MV cable system diagnostics, a wide range of commercial technical offerings exist amongst equipment vendors and service providers, involving different types of voltage sources and different levels of system complexity. This happens in a context of lack of standardization, and often a lack of understanding of the different factors that can influence the accuracy of PD measurements. Such factors can result in the very ‘real’ probability that the use of one MV cable PD measurement strategy / system versus another could lead to diagnostic inaccuracy, and ultimately different MV cable field PD assessment outcomes for the same test situation. This paper thus summarizes and illustrates the importance of testing context, diagnostic accuracy, and key technical considerations upon which MV cable field PD measurements are critically dependent. The objective is to assist non-expert end-users engaged in the specification, procurement, or execution of a MV cable PD field condition assessment programs to understand their requirements, objectively evaluate available options, and ultimately select the most appropriate PD testing strategy for their specific context.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.020 | 0.095 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".