Wavelet based on-line disturbance detection for power quality applications
Why is this work in the frame?
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Full frame distilled prediction
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.
- Candidate categories
- Meta-epidemiology (narrow)
- Consensus categories
- none
- Domain
- Candidate signal: noneConsensus signal: none
- Study design
- Candidate signal: Simulation or modelingConsensus signal: Simulation or modeling
- Genre
- Candidate signal: EmpiricalConsensus signal: none
- Teacher disagreement score
- 0.969
- Threshold uncertainty score
- 1.000
- Validation status
machine_predicted_unvalidated·codex-gemma-dda1882f352a
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| 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 | 0.000 | 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)
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
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.
- Teacher spread
- 0.234 · how far apart the two teachers sit on this one work
- Validation status
score_only:v0-immature-baseline· verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it
Abstract
This paper introduces a new online voltage disturbance detection approach based on the wavelet transform. The proposed approach: (1) identifies voltage disturbances; and (2) discriminates the type of event which has resulted in the voltage disturbance, e.g. either a fault or a capacitor-switching incident. The proposed approach is: (1) significantly faster; and (2) more precise in discriminating the type of transient event than conventional voltage-based disturbance detection approaches. The feasibility of the proposed disturbance detection approach is demonstrated based on digital time-domain simulation of a power distribution system using the PSCAD/EMTDC software package.
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.
The record
- Venue
- IEEE Transactions on Power Delivery
- Topic
- Power Quality and Harmonics
- Field
- Engineering
- Canadian institutions
- University of Toronto
- Funders
- not available
- Keywords
- Disturbance (geology)Disturbance voltageWaveletWavelet transformElectric power systemTransient (computer programming)VoltageFault detection and isolationCapacitorTransient voltage suppressorFault (geology)Electronic engineeringEngineeringComputer sciencePower (physics)Control theory (sociology)AC powerVoltage optimisationArtificial intelligenceElectrical engineeringControl (management)
- Has abstract in OpenAlex
- yes