Voltage unbalance treatment for distribution network with massively connected distributed generators
Bibliographic record
Abstract
Large scale interconnection of single-phase distributed generation (DG) with utility distribution grid can cause current unbalances resulting in voltage unbalances. The methods of Newton-Raphson and Gauss-Seidel are generally used for power flow's calculation. However, these methods are sometimes complex and their convergences may be long. The iterative process of Newton-Raphson method, using the admittances matrix is practically independent of bus bars number. The calculation's time of Gauss-Seidel method increases almost proportionally with the bus bars. In addition, the calculation's time of the Jacobien's matrix and each iterative process are considerably long for the Newton-Raphson method. In this paper, a method based on the impedance order reduction and identification of the power flow's direction is presented. Analytical and simulation studies were performed in order to validate the accuracy of the proposed method. For an application network model, the voltage unbalances are calculated. The results are compared with those of two commercial softwares.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".