Zero Sequence Current-Based Synchronized Lissajous Curves for Event Detection in Distribution Networks Using Waveform Measurement Units
Bibliographic record
Abstract
Distribution Networks (DNs) are advancing by integrating digital technologies and renewable energy to enhance efficiency and resilience. In this context, waveform measurement units (WMUs) are seen as a potential solution for detecting transient events and ensuring grid stability due to their ability to provide high-resolution data. Inspired by WMUs' capabilities, this paper proposes a novel approach for event detection using synchronized Lissajous curves using the zero sequence of current waveforms. This data-efficient method leverages zero-sequence components to sensitively detect asymmetrical events. An index (K) is proposed based on peak Euclidean distance values between samples and does not require prior knowledge of the distribution network topology. The proposed method is able to detect high-impedance faults and capacitor bank switching directly in the time domain without pre-processing. Testing on an IEEE-34 bus system demonstrates the robustness and adaptability of the proposed index K, which remains stable under normal conditions and shows distinct responses to each event type.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".