Mitigation of Fault Induced Delayed Voltage Recovery (FIDVR) by PV-STATCOM
Bibliographic record
Abstract
This paper presents a novel day-and-night control of large PV solar farm as STATCOM, termed PV-STATCOM, for FIDVR alleviation. The proposed control involves dynamic modulation of both reactive and real power during day, and of reactive power during night. The active and reactive power output of solar farm are based on the sensitivity of solar plant voltage to active and reactive power injections. Enhanced voltage control up to utility Transient Overvoltage Limit is utilized. Eigenvalue analysis is done to study the sensitivity of proposed controller performance to various power system and PV plant parameters. Extensive PSCAD simulation studies of FIDVR are performed in a realistic power transmission system with large-scale PV plant and comprehensive IM loads. It is shown that the proposed PV-STATCOM control: (i) mitigates FIDVR even if solar farm is located more than 100 km from motor loads, (ii) is more effective than reactive power support required by German Grid Code, (iii) is equally effective as a STATCOM connected locally at motor loads, and (iv) stabilizes motors at night which is beyond Grid Code requirements. The PV-STATCOM potentially provides significant cost savings to utilities and new revenue stream for solar plants in providing 24/7 FIDVR mitigation service.
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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 | 0.000 |
| 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 | 0.001 | 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".