Power Flow Study of MT-HVDC Grid Compensated by Multiport Interline DC Power Flow Controller
Bibliographic record
Abstract
DC power flow controllers (DCPFCs) are emerging devices to control power flow in voltage source converter (VSC)-based multi-terminal HVDC (MT-HVDC) grids. In this paper, a novel Newton-Raphson (NR)-based DC power flow solver (DCPFS) is proposed to solve the DC power flow problem (DCPFP) by using a novel multiport interline DC power flow controller (MIDCPFC), where physical and control state variables of the whole system (MIDCPFC and MT-HVDC grid) are modified simultaneously to achieve predetermined control objectives. The static model (SM) and the power injection model (PIM) of the considered MIDCPFC have been derived and their equations are embedded within the proposed DCPFS. Since there are no fictitious buses in the proposed DCPFS, the original conductance matrix of the system and its symmetry are preserved, and only minor modifications are needed on the original system's Jacobin matrix. It is very straightforward to implement the proposed DCPFS as the voltage of intermediate capacitor of the MIDCPFC is treated as an independent variable, and thus, an external process to control its value is not needed. In this study, comprehensive models have been proposed to model losses of the MIDCPFC and VSCs for the first time; and the shunt conductance of HVDC lines have also been considered. Finally, a modified 15-bus MT-HVDC grid is proposed for verification purposes. The obtained results verify the accuracy and efficacy of the proposed concepts, models, and formulations of this study.
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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 | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".