A Scheme for Position and Capacity Determination of Distributed Generation Considering Load Distribution and System Voltage Stability
Bibliographic record
Abstract
The purpose of this study was to investigate a simplified distributed generation (DG) Position and Capacity Determination model (DG-PCD model) based on the coupling relationship between load distribution and voltage stability in the distribution network. First, based on the relationship between voltage stability and system equivalent impedance and the relationship between system equivalent impedance and load distribution, the relationship between voltage stability and load distribution is deduced, and the concept of influencing impedance mode is proposed and used in DG site selection. Then, build a DG-PCD model considering voltage stability, active power loss and line thermal stability margin, and use genetic algorithm (GA) to solve the model. Finally, an improved IEEE33-node system calculation example is analyzed. The results show that compared with the existing methods, the proposed method can get better results faster. This Proposed method not only simplifies the DG-PCD model, but also quantifies the relationship between voltage stability and load distribution. This provides a new reference index for the voltage control of the power grid.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".