A Sequence-Component-Based Power-Flow Analysis for Unbalanced Droop-Controlled Hybrid AC/DC Microgrids
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Bibliographic record
Abstract
This paper proposes a generalized and efficient power-flow algorithm for islanded hybrid ac/dc microgrids. The algorithm considers the microgrid operational aspects, i.e., absence of a slack bus, unbalanced ac subgrid, droop-controlled ac and dc voltages and ac frequency, and coupling between the ac frequency and dc voltage through interlinking converters. To attain high computational efficiency, the algorithm adopts three features. First, it models the ac subgrid elements in sequence components, thereby dividing the subgrid's set of equations into three smaller sets for faster parallel solution. This approach also accurately represents the different types of ac distributed generators. Second, the algorithm sequentially solves for the power-flow variables of the ac and dc subgrids, thus reducing the number of equations to be solved simultaneously, once again for further computational cost alleviation. Third, the algorithm implements the quadratically convergent Newton-Raphson technique to solve the decoupled sets of equations. The proposed algorithm is validated through comparisons with time-domain simulations, in MATLAB/Simulink, for test hybrid ac/dc microgrids of different configurations. Moreover, three case studies are introduced to examine the proposed algorithm's effectiveness in solving large-scale microgrids, to investigate its limits-enforcement capabilities, and to evaluate its performance as compared to conventional methods.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
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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 it