D-based Predictive Control for Enhancement of Distribution System Stability and Operation
Bibliographic record
Abstract
The stability of bulk power electricity systems has been well studied for decades. Conversely the stability study for newly deregulated distribution power systems with dispersed generations from small conventional or alternate energy sources has been virtually limited. Stability concerns, however, increase rapidly with today's growing demands for open access to power systems for electricity generation and trading, facilitated by new government deregulations. This paper presents a novel generator control based on step-ahead predictive methodology and state-of- the-art real-time digital signal processing (DSP) technology. This DSP-control-based (D-based) Predictive Control is built upon optimization of a specific performance index defined as a weighted combination of generator voltage deviation, mechanical and electrical torques mismatch, incremental generator speed, etc. This paper demonstrates that the D-based Predictive Control can significantly improve the stability and operational coordination of distribution systems particularly those with dispersed generations, open access operations, or weakly connections to bulk power systems.
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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) | 0.000 | 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) | 0.001 | 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".