An Intelligent-Based Technique for Locating Switched Capacitors in the Distribution Systems Using High Frequency Transients
Bibliographic record
Abstract
With the increase of the usage of sensitive loads such as adjustable speed drives, utilities and customers paid more attention to power quality (PQ) problems. Switching of shunt capacitors is one of the major power quality problems that generates wide band of voltage and current transients which harmfully affect these types of sensitive loads. This paper presents an accurate technique for locating switched capacitors in the industrial distribution systems. The technique relays on combining the three phase currents at the customer side to construct one modal current signal, and then the discrete wavelet transform (DWT) is used to capture the high frequency current transients contained in this modal signal. The energy contained in the high frequency current transients and the transient duration, converted into number of samples, for two DWT decomposition levels are used to feed a decision tree (DT) classifier to classify the switched capacitor. The simulations are performed using PSCAD/EMTDC and the results are then interfaced to See5.00, where the DT where constructed and trained. It is found, based on simulation, that the technique gives reliable results for locating switched capacitors.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| 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)
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".