Prediction and Control of Transient Instability Using Wide Area Phasor Measurements
Bibliographic record
Abstract
This thesis presents a novel technique for prediction of the transient stability status of a power system following a large disturbance such as a fault, and application of the tech-nique for subsequent emergency control. The prediction is made based on the synchro-nously measured samples of the magnitudes of fundamental frequency voltage phasors at major generation/load centers. The voltage samples are taken immediately after a fault is cleared and used as inputs to a binary classifier based on support vector machines to iden-tify the transient stability condition. The classifier is trained using examples of the post-fault recovery voltages (inputs) obtained through simulations and the corresponding sta-bility status (output) determined using a power angle-based stability index. Studies with the New England 39-bus test system indicate that the proposed algorithm can correctly recognize when the power system is approaching transient instability. The proposed sys-tem is then applied to Venezuelan power system and Manitoba Hydro power grid to demonstrate the applicability for large practical power systems. Performance of the pro-posed transient stability prediction scheme under the presence of asymmetrical faults, voltage sensitive loads, unlearned network topologies and measurement noise was found to be satisfactory. Once an impending transient instability situation has been detected, appropriate emer-gency control strategies are triggered to minimize the impact of this on the safe operation of the network and reduce the possibility of a blackout. This thesis examines two differ-ent emergency control schemes: a) A fuzzy logic based emergency load and generator shedding scheme and b) A high voltage direct current (HVdc) power order reduction scheme based on synchronized phasors measurements. These strategies were developed for two power systems with contrasting characteristics: one for the Venezuelan power system which is a conventional power system completely based on alternating current (AC) transmission, and the other for the Manitoba Hydro network which heavily depend on long HVdc transmission for power transfer. The proposed wide area control systems demonstrated good performance on the Venezuelan and Manitoba Hydro power grids.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 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 | 0.001 |
| 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".