Load Flow Analysis With Newton–Raphson and Gauss–Seidel Methods in a Hybrid AC/DC System
Bibliographic record
Abstract
In this study, a dc system was added to the IEEE 33-bus radial distribution system (RDS) test system using voltage-source converters (VSCs), and a hybrid ac/dc system was designed. For this designed system, a load flow analysis was made under the MATLAB platform. The Gauss-Seidel and Newton-Raphson methods, which are widely used in load flow analysis, are used. Comparisons of these methods were made according to the number of iterations, total line losses, and active and reactive powers generated and consumed in terms of different tolerance values. In the results obtained from the load flow analysis studies, the powers produced by the generator are calculated as close to each other according to the load demands for both methods. However, it was seen that the least iteration number and the least power loss were obtained by the Newton-Raphson method. According to the results obtained as a result of applying the power flow algorithm used in the hybrid ac/dc system, it has been proven that this algorithm is successfully applied to the solution of a power system problem. In addition, the results show that the designed system is accurate and reliable.
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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) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".