D-based Predictive Control for Enhancement of Distribution System Stability and Operation
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.002 | 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 it