Current-source converter based SSSC: modeling and control
Bibliographic record
Abstract
The static synchronous series compensator (SSSC) is a series connected FACTS controller, which is capable of providing reactive power compensation to a power system. The output of an SSSC is a series injected AC voltage, which leads or lags the line current by 90/spl deg/, thus emulating a controllable inductive or capacitive reactance. SSSC can be used to reduce the equivalent line impedance and thus enhance the active power transfer capability, of the transmission line. SSSC is conventionally realized with a voltage-source converter. In this paper, an SSSC based on the current-source converter topology is proposed. In this structure, the DC-side current is regulated to a value larger than the peak value of the maximum line current. The injected voltage is controlled according to the desired reactive power compensation. The decoupled state-feedback control for the injected voltage with a separated DC current control is applied to the proposed system. The advantages:of the proposed scheme include fast dynamic response and high quality of current and voltage waveforms. The proposed SSSC has been simulated using the PSCAD/EMTDC package. The simulation results show that excellent current and voltage waveforms as well as very short response times can be obtained while operating at a low switching frequency. This makes the proposed scheme suitable for high power applications.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| 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".