Optimal 24-hr utilization of a PV solar system as STATCOM (PV-STATCOM) in a distribution network
Bibliographic record
Abstract
This paper presents a novel optimal utilization of photovoltaic solar system as STATCOM for voltage regulation and power factor correction during both nighttime and daytime. The PV solar system conventionally generates real power during the day but the entire asset remains idle at night. This novel PV solar system operated as STATCOM is termed PV-STATCOM which utilizes the entire inverter capacity in the night and that remaining after real power generation during the day for accomplishing various STATCOM functionalities. Bluewater Power Corporation in Sarnia, Canada, is going to showcase this new concept of optimal utilization of PV solar system on a 10kW PV system in its network. The controller for the PV-STATCOM is being developed in the university lab and will be installed in the distribution utility network. A simulation model for the 10kW PV-STATCOM and the Bluewater Power distribution system network is developed in PSCAD software. This paper presents the steady state and transient performance of the PV-STATCOM controller for voltage regulation and power factor control both during nighttime and daytime. This proposed PV-STATCOM if connected at the terminals of an industrial customer having induction motor loads can help improve power factor and avoid potential penal tariffs over a 24-hour period, in addition to generating revenues due to sale of real power during the day.
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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 | 0.000 | 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".