Bibliographic record
Abstract
a Advanced inverters 35 see also Smart inverters Arresting period 5 c Capability characteristics of combination of PV, BESS and EV charging system 440 of distributed energy resource 35-36 Conservation voltage reduction (CVR) 439 Control coordination 369, 372 Control signals for mitigation of subsynchronous resonance (SSR) 241 for power oscillation damping (POD) 121-122 Critical inertia 8 d Delays in solar PV plant 86 communication delays 86, 189-190, 265 filtering delays 86 measurement delays 86 Design procedures PV-STATCOM current controllers 99, 271 PV-STATCOM FFR controller 283 PV-STATCOM PCC voltage controller 271 PV-STATCOM POD controller 224, 234, 282 PV-STATCOM SSR damping controller 257, 416 VSC AC filter capacitor 84-85 VSC DC link capacitor 84 VSC DC link voltage controller 100 Distributed energy resource (DER) 35 active power (watt) priority mode 40 modeling 104 reactive power (var) priority mode 41 performance categories 38 reactive power capability 39-40 sign convention of active and reactive power 38 Duck chart 15 f Factors impacting control interaction among PV-STATCOM and Type 3 wind farm for damping SSO 413-423 smart inverters and FACTS/HVDC systems 424 smart inverters and on load tap changing transformers 373-380 Factors impacting control interaction among smart inverters with volt-var function measurement delays 398, 401, 410, 412 response time of inverters 393-396, 402, 411, 412 slope of volt-var function 393-396, 401 system strength 401, 402 volt-watt function 402 X/R ratio 402 Factors impacting control interaction of SVCs 381-385 Factors impacting control interaction within a smart inverter 392-393 controller gains 392-393, 398 filter time constants 392-393 465 Smart Solar PV Inverters with Advanced Grid Support Functionalities, First Edition.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.708 | 0.610 |
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".