SSR Mitigation With a New Control of PV Solar Farm as STATCOM (PV-STATCOM)
Bibliographic record
Abstract
This paper presents a novel control of a large-scale PV solar farm as STATCOM, termed PV-STATCOM, for alleviation of subsynchronous resonance (SSR) in a steam turbine driven synchronous generator connected to a series compensated transmission line. During nighttime, the PV solar farm can operate as a STATCOM with its entire inverter capacity for SSR mitigation. During daytime, if a system fault triggers SSR, the solar farm autonomously discontinues its normal active power generation and releases its entire inverter capacity to operate as PV-STATCOM for SSR prevention. Once the subsynchronous resonances are damped, the solar farm returns to its normal real power production. Electromagnetic transients studies using EMTDC/PSCAD are performed to demonstrate that a solar farm connected at the terminals of synchronous generator in the IEEE First SSR Benchmark system can damp all the four torsional modes at all the four critical levels of series compensation, and return to normal PV power production in less than half a minute. This proposed PV-STATCOM technology can either obviate or reduce the need of an expensive flexible ac transmission system device to accomplish the same objective. Furthermore, this technology is more than an order of magnitude cheaper than a conventional static var compensators or STATCOM of similar size.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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".