Neural-Predictor-based Data-Driven Predictive Control for 100kVA, 4.16/0.48 kV Solid-State Transformer for Smart Distribution Grids
Bibliographic record
Abstract
A data-driven predictive controller (DDPC) is proposed for the solid-state transformer (SST) to manage the power flows in a distribution network. The proposed DDPC is derived to be used as a potential substitute to vector control which the design requires knowledge of the grid-connected system. The strategy uses input and output data from the system, the ultra-local model based on a neural network for modeling the dynamic of the current. The neural network enhances the robustness of the controller for nonlinear systems and external disturbances. To improve the current accuracy of the controller, a discrete space vector is used to discretize the operating region of the converter into an equivalent virtual multi-level converter. Simulation results demonstrate the effectiveness of the proposed control in managing the power flows in a medium-voltage distribution network.
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How this classification was reachedexpand
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) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| 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)
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 teacher head, 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".