Real-Time Digital Time-Varying Harmonic Modeling and Simulation Techniques
Bibliographic record
Abstract
With the growing importance of power quality problems to electric utilities and customers, there is an increased focus on the search for new tools and techniques for accurate analysis and resolution of such problems. This paper reviews currently available techniques for the modeling and simulation of time-varying harmonics in real-time. Following a brief summary of the currently used off-line harmonics modeling and simulation methods, the principles and system element representations using wave digital filter (WDF) and discrete wavelet transform (DWT) methods are discussed. Hardware and software architectures of real-time network simulator (RTNS), HYPERSIM, and PC-cluster based real-time simulator are presented. Towards the end, two case studies are given to demonstrate the real-time analysis of time-varying harmonics generated by a three-phase arc furnace using the PC-cluster based real-time simulator, and a real-time hardware-in-the-loop (HIL) equipment testing using the real-time digital simulator (RTDS).
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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 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".