The Formulation of a Power Flow Using $d\text{--}q$ Reference Frame Components—Part II: Unbalanced $3\phi$ Systems
Bibliographic record
Abstract
This paper presents the formulation and testing of the extended d-q-axis power flow (DQPF) method to analyze power systems that have buses with unbalanced 3φ voltages. The extended DQPF method is based on converting a 3φ system into three networks, which are defined using the d-axis, q-axis, and 0-axis voltage and current components. Each of the three networks is modeled by nodal equations, where the nodal voltages and admittance matrix determine the currents flowing in that network. Moreover, the apparent power mismatches are used (instead of active and reactive power mismatches) in order to reduce computational requirements. This approach offers an accurate representation of buses with unbalanced 3φ voltages resulting from load unbalances or asymmetrical impedances of 3φ transmission lines. The extended DQPF method is implemented for performance testing on different power systems that have buses with unbalanced 3φ voltages. Performance results show good accuracy, fast convergence, and minor sensitivity to the source of voltage unbalance. In addition, performance results reveal that the extended DQPF requires less iterations and lower memory requirements to obtain power flow solutions than other 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 | 0.000 |
| 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".