Setting under-frequency relays in power systems via integer programming
Bibliographic record
Abstract
The deviation of the frequency of a power system from its nominal value is a reflection of the mismatch between generation and load. Such deviations are serious and must be monitored and controlled very closely. One major impact of operating outside a narrow range around the nominal frequency is that generators can be damaged. To avoid this, manufacturers set time interval limits for under-frequency operation and when such limits are exceeded, the generator trips. However, unless generator tripping is coordinated with some accompanying load shedding, the system inability to supply its load can be exacerbated resulting in an even worse frequency deviation. Under-frequency load shedding (UFLS) is designed to protect the power system from events leading to a sudden drop in system frequency, when the primary frequency regulation built into the generation system is not enough to bring the frequency back to nominal. Under-frequency load shedding disconnects blocks of load when the frequency drops below given thresholds. However, the conventional design of UFLS schemes is primarily based on experience about the behavior of the system. Basically, trial relay settings are proposed, tested, and revised until a successful UFLS scheme is obtained. This process is tedious, not very systematic, and usually leads to shedding conservative amounts of load. This thesis presents a mixed-integer linear programming formulation of the UFLS relay setting problem. The goal is to render the design of UFLS more systematic, less dependent on trial and error, and less conservative in terms of the amount of load shed.
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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 | 0.003 | 0.006 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".