Assessment of Space Weather Impacts on New Zealand Power Transformers Using Dissolved Gas Analysis
Bibliographic record
Abstract
Abstract Space weather can have major impacts on electrical infrastructure. Multiple instances of transformer damage have been attributed to geomagnetic storms in recent decades, for example, the Hydro Quebec incident of 1989 and the November 2001 storm in New Zealand. While many studies exist on the impacts of geomagnetic storms on power transformers in New Zealand, no studies exist that employ Dissolved Gas Analysis (DGA) techniques to relate geomagnetic storms to transformer gassing. A relationship has been reported between geomagnetic activity and DGA for South Africa, while none was found in a recent study in Great Britain. This paper attempts to examine this research question by examining dissolved gas data across eight power transformers in different substations in New Zealand from 2016 to 2019. Case studies were conducted which analyzed the DGA readings of each transformer alongside horizontal magnetic field component rate of change measurements at Eyrewell across six geomagnetic storms. These case studies were then augmented with an analysis of the entire data set where magnetic field measurements were compared with individual gas rates to establish a correlation between gas production and geomagnetic activity. Analysis of the results of this study concluded that no link had been found between the production of combustible gasses in a transformer and geomagnetic activity during the observation period. However, we note our dissolved gas analysis was largely in a geomagnetically quieter period, which may limit our analysis. The production of combustible gasses is not correlated to geomagnetic storms for the time period and transformers analyzed.
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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 | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
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".