Versatile control strategy of the unified power flow controller (UPFC)
Bibliographic record
Abstract
The unified power flow controller (UPFC) is a third generation flexible AC transmission system (FACTS) that uses solid state electronics to control the flow of power through lines in a faster and more economical fashion. In this paper, we propose a versatile control strategy of the UPFC that joins shunt active filtering and system balancing capabilities to real time power flow control into one device. To achieve optimal UPFC behaviour in the different mode, the controllable system parameters are determined on-line based on the local instantaneous voltage and current measurements. The strategy is based on the theory of instantaneous parameters defined in the positive, negative and zero sequence with real and imaginary components. The power flow calculation should not involve any conventional definitions of power, the Fortescus transformation is used to conform the relation of the instantaneous active and reactive power with the steady state power values. The control algorithm compensates the oscillated part of the instantaneous active power and reactive power component to achieve the harmonics compensation without any RMS value calculation of voltage and current. The UPFC and the transmission system model has been developed in Simulink environment for the digital simulation to demonstrate the versatility of the new control strategy. Interesting results have been obtained, which are presented in this paper.
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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 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| 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".