Asymmetric Events Detection and Classification Using Zero Sequence Current-Based Synchronized Lissajous Curves from Waveform Measurement Units
Bibliographic record
Abstract
Accurate detection and classification of power quality events is essential for maintaining reliability and situational awareness in modern distribution networks. This paper proposes a novel method for detecting and classifying asymmetric power quality events using synchronized Lissajous curves. The proposed framework operates exclusively on current waveforms recorded by waveform measurement units (WMUs). Synchronized Lissajous curves are constructed by plotting zero-sequence currents from two WMUs, capturing geometric patterns that reflect the nature and severity of disturbances. Event detection is carried out using the Maximum Consecutive Euclidean Distance between successive samples, and the classification relies on the Maximum Origin-Based Distance. The performance of the proposed method is evaluated on the IEEE 34-bus test feeder across multiple asymmetric events, including high impedance faults (HIFs), single-phase capacitor bank switching, and unbalanced load-switching. Results show that the approach is able to distinguish the type of asymmetric events, requiring minimal computational resources, and operates effectively without voltage measurements.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.004 | 0.002 |
| 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 | 0.001 |
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".