A Support Vector Regression Based Model Predictive Control for Volt-Var Optimization of Distribution Systems
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Bibliographic record
Abstract
This paper proposes a support vector regression (SVR)-based model predictive control (MPC) for the volt-var optimization (VVO) of electrical distribution systems. First, measurement data from a few days of operation of a distribution system, gathered using advanced metering infrastructure (AMI), are used to train an SVR model of the system. The trained model is then employed by the MPC in a closed-loop control scheme to control capacitor banks and tap changers of the distribution system so that the power loss is minimized, and voltage profiles are maintained within a specific range. In contrast to the many existing VVO methods, the proposed scheme does not require any circuit-based simulations for its operation, nor does it assume that the distribution system is radial. The simulation results of applying the proposed SVR-based MPC to IEEE123 bus test feeder proves that despite its measurement-based feature, the proposed approach is capable of providing close to optimal solutions to the VVO problem. The simulation results also suggest a satisfactory outcome of the proposed approach in controlling meshed grids or in the presence of distributed energy resources (DERs).
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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) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 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) | 0.000 | 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 it