General Multi-Phase Element-Based Load Flow for AC–DC Power Systems
Bibliographic record
Abstract
Deep electrification of energy systems is expected in the future and distribution systems are pegged to grow several folds interconnecting numerous distributed energy resources such as wind, solar and storage. Accordingly, certain sections of the distribution systems will be meshed to provide a higher reliability due to their critical and enlarged role in energy systems. Furthermore, just like solar farms that internally use DC networks and interface with unbalanced multi-phase AC systems, it is expected that DC networks will continue to expand within AC distribution systems due to advancements in power electronic solutions. Therefore, a multi-phase AC–DC load flow that models both meshed and radial multi-phase topologies is an important analytical tool. Thus far, the literature reports a limited number of methods and restricted commercial grade solutions. To address this challenge, a novel multi-phase AC–DC load flow method is proposed. This method handles diverse multi-phase AC - DC network topologies and component types such as branches, transformers and controllable converters using an element-based modeling technique modeling self and mutually induced voltage drops as dependent voltage sources. Tested across various systems, this method has proven to be robust, computationally efficient, and faster than traditional node-based AC–DC load flow method, while maintaining the same level of accuracy and monotonic convergence.
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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) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.014 | 0.003 |
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".