Fault Location in High Voltage Shunt Capacitor Banks
Bibliographic record
Abstract
High Voltage Shunt Capacitor Banks (HV-SCBs) are critical substation assets that are primarily used to improve power factor and thus power transmission capability. Over temperature, over voltages, manufacturing defects can cause internal failures of capacitor elements. With today’s sensitive protection available in numerical relays these failures will be detected and SCBs will be taken out of service. But determining the phase and section in which capacitor elements have failed is important for utilities to decrease downtime of SCBs. This thesis proposes methods to address the challenges in fault location of SCBs. The proposed methods apply calibrating factors that compensate for pre-existing inherent unbalances, and system voltage unbalances. They can mitigate the gradual capacitance change due to temperature effects or natural aging. The presented fault location methods are further enhanced to detect the number of failed elements. The proposed methods can be integrated into common unbalance protection of the multi-functional numerical SCB relays and put forth solutions to condition monitoring of SCBs. Advance maintenance alarms for fuse saving in externally-fused SCBs, and reducing the search space for the faulty cans with fuseless or internally-fused designs are the significant outcomes of the proposed methods. The proposed methods are simulated in a relay modeled in MATLAB and the voltages/currents are played back from COMTRADE records of PSCAD electromagnetics transient simulations. Detecting consecutive failures, ambiguous failures, and delivering the number of failed elements for each alarm in fault reports are valuable to the utility community and have been verified for the proposed methods.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| 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".