The Formulation of a Power Flow Using <inline-formula> <tex-math notation="LaTeX">$d-q$</tex-math> </inline-formula> Reference Frame Components—Part I: Balanced <inline-formula> <tex-math notation="LaTeX">$3\phi$ </tex-math> </inline-formula> Systems
Bibliographic record
Abstract
This paper develops a new approach for formulating the power flow for power systems. The developed approach is based on expressing the active and reactive power injections at each bus in a power system using the d-q-axis components of bus voltages and admittance matrix. The new power flow formulation produces a scaled Jacobian matrix, which can offer a fast convergence to the solution. The proposed power flow formulation can also model bus type conversions without affecting its accuracy or fast convergence. In addition, the d-q-axis power flow (DQPF) offers a simplified and reliable representation of photovoltaic buses that have distributed generation units (DGUs). The DQPF is implemented with a step-by-step procedure for performance evaluation on several power systems operated at different conditions. Performance results demonstrate fast convergence, reduced computations, and minor sensitivity to the number of buses, loading conditions, and levels of DGU penetration. Furthermore, performance results show that DQPF power flow can attain solutions in less iterations than Newton-Raphson, Fast Decoupled, and Iwamoto power flow methods.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.017 | 0.004 |
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".