Simulated Testing Algorithm for µPMU Full Observation of Balanced Radial Distribution Grid
Bibliographic record
Abstract
Today's electric power distribution systems with development of distributed energy resources introduce variability, uncertainty, and opportunities to recruit diverse resources for grid services. Multiple resources on each feeder have more complex impacts on the circuit behavior that can be observed with voltage and current phase angle variations. Micro Phasor Measurement Units (μPMUs) take time-synchronized measurements of voltage, current and frequency that can tell grid operators what is happening, where, and when. This paper presents a new μPMUs power flow algorithm for complete observation of balanced radial distribution grid. This algorithm calculates all voltages in both high and low voltage buses, currents in all branches, line active and reactive power flow in all branches and total active and reactive power losses in the grid. This algorithm provides high quality data for distribution planners and operators, which will translate into better model accuracy and thus better results from distribution analysis tools. To test the validity of proposed algorithm, backward / forward sweep power flow program is developed and tested by ETAP software.
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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 | 0.003 |
| 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 | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.008 | 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".