Hilbert Huang Transform Based Online Differential Relay Algorithm for a Shunt-Compensated Transmission Line
Bibliographic record
Abstract
A differential protection scheme, based on real-time analysis of a time-frequency based technique, is proposed for a transmission line with a midpoint-connected static synchronous compensator (STATCOM). The first intrinsic mode function obtained from the decomposition of current signals from both ends of the transmission system, processed through an ensemble empirical mode decomposition technique, is used to estimate the discrete Teager energy (DTE) through online Hilbert-Huang transformation. The differential DTE from both ends of the line is used to detect the exact faulty phase. For analysis, a midpoint STATCOM compensated transmission line model is considered and simulated using EMTDC/PSCAD. Test cases, such as high fault resistance, fault inception angle, reverse power flow, current transformer saturation, and variation in source impedance, are generated for different operating modes of the STATCOM. The method is also tested for a cross-country fault in a double-circuit transmission system. Comparative assessment reports with other conventional approaches verify the reliability, speed, and feasibility of the proposed method.
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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) | 0.001 | 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 | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".