Harmonic/Interharmonic Detection and Estimation based-SimPowerSystems for Machine Learning
Bibliographic record
Abstract
This paper presents new methodologies for the detection and estimation of harmonics and interharmonics in data sets, using specialized technology libraries from Simscape Power Systems (SPS). A dedicated MATLAB script has been developed to analyze each phase by simulating short-circuit faults on a standard IEEE reference base. Various direct, inverse and homopolar harmonic components are generated and randomly injected at different points of the test network. During simulation, current and voltage signals from the buses are recorded at a frequency of 20 kHz, creating a rich database for automatic learning. This method, which can be extended to more complex networks and higher harmonic orders, extracts precise characteristics in terms of frequency, amplitude and onset/disappearance time. It is also adaptable to non-standard networks modeled in Simscape. The proposed approach thus modernizes and enhances advanced harmonic phase detection (HPD) and estimation (HPE) techniques, while implementing an efficient time-delay (TD) method for fault detection. Finally, the resulting database provides a reliable basis for training robust models and implementing critical protection functions.
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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 | 0.000 | 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) | 0.000 | 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".