A new Fuzzy based Total Demand Distortion Factor for nonsinusoidal situations
Bibliographic record
Abstract
When considering one single range of short circuit level, the values of "Total Demand Distortion" TDD are enough to quantify harmonic distortion in a certain current waveform. When considering multiple ranges of short circuit levels the TDD is unable to determine whether the distortion is within the acceptable limits or not. In this paper, a new Fuzzy Total Demand Distortion Factor (FTDDF) is proposed. The FTDDF indicates the level of distortion in the current waveform or (how close is the waveform to a pure sinusoidal wave shape,) and also allows deciding whether the distortion contained in the current is within the acceptable limit or not. Moreover, the use of a Fuzzy Inference System (FIS) has the advantages of being simple, easy to implement and contains its knowledge base. The proposed FTDDF is sensitive to the TDD and short circuit level changes in all distortion cases in sinusoidal and nonsinusoidal situations. Therefore it will be very useful for many applications such as power quality evaluation, cost-benefit analysis of PQ mitigation techniques and setting penalty tariffs for customers generating harmonics.
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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".