Three-Phase Unbalanced Power Flow Using a <formula formulatype="inline"> <tex Notation="TeX">$\pi$</tex> </formula>-Model of Controllable AC-DC Converters
Bibliographic record
Abstract
Microgrids are unique in that they can combine unbalanced three-phase systems with other AC and DC network sections, which may include a range of renewable energy sources, energy storage elements, and controllable AC-DC converters. Existing unbalanced power flow techniques such as the Ladder Iterative Technique and the three-phase Newton-Raphson (NR) method can analyze microgrids in sections but lack a complete system representation. Hence, there is a need for a power flow algorithm that considers the complete system model and solves it. A novel π-model of a controllable AC-DC converter and a single set of power balance equations for modeling a grid comprising multiple three-phase AC and DC sections is proposed. The π-model of a controllable AC-DC converter enables its inclusion into the network bus admittance matrix (YBUS) along with three-phase AC and DC network sections. Verification of the π-model is also described in the paper. The results of power flow studies with three-phase balanced and unbalanced AC and DC network sections are presented. The outcome of the π-model verification study and power flow study show that the proposed π-model is consistent and accurate. While the proposed model was developed for microgrids, it is applicable for all power system analysis 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 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.013 | 0.002 |
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".