Sparce Harmonic Filter Based-On Hybrid Dictionary Learning for Power Systems Resilience
Bibliographic record
Abstract
This paper explores innovative methods to mitigate harmonic pollution in power systems using a Sparse Harmonic Filter (SHF). A harmonic generator, modeled in MA TLAB/Simulink, was integrated at various points within the system. Spectral analysis identified frequency components in the signals. The SHF, developed through supervised learning, leverages a dictionary of cosine, sine, and identity atoms trained offline to minimize harmonic content. To benchmark its performance, two conventional hybrid filters were also designed. Total Harmonic Distortion (THD) was measured both upstream and downstream of the fault inj ection point and filters. Testing on the IEEE 9-bus network revealed that the SHF achieved a THD of 0.24% at Bus 4, significantly outperforming conventional filters, which exhibited 11.26% THD at the same bus.
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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".