Root Cause Analysis of Over-Current Ground Relay Tripping During Energizing Parallel Autotransformers
Bibliographic record
Abstract
In this paper, the root cause analysis of excessive zero-sequence currents is conducted when energizing parallel autotransformers in a power system. The studied system is a large international airport in Canada, where its power is supplied by both utility and local distributed generation (DG) feeding the load through two parallel autotransformers. Recent field records indicated that when the utility, local generators and the first autotransformer were in operation, and the second autotransformer was already energized from its low voltage side, at this moment, closing the circuit breaker on the high voltage side of the second autotransformer caused rich third and ninth order harmonic currents and the subsequent over-current ground relay (51G) tripping. Through this research, it is determined that the root cause of the problem is the saturation of the two autotransformers. The PSCAD/EMTDC simulation is conducted for the system, and the simulation model is benchmarked using field measurement data. Two methods are developed in this paper to calculate the maximum RMS zero-sequence current flowing through the two autotransformers: 1) an analytical based simplified equivalent circuit method and 2) a curve fitting based mathematical equation method. The solutions to prevent misoperation of the ground relay are proposed and their effectiveness is validated.
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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 | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| 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".