Synchrophasor-based state estimation for online voltage stability monitoring in power systems
Bibliographic record
Abstract
Power distribution systems are faced with rising operational challenges and require continuous stability monitoring as the integration of renewable energy resources is increasing and the load demand is rapidly growing.In order to provide timely information about any impending grid problems to system operators, this thesis focuses on the analysis and monitoring of voltage stability on distribution system side.The implementation of synchrophasors in distribution systems enhances the situational awareness of the system.Synchrophasor measurement offers increased visibility, faster response time and more reliable state estimation, which provides a unique opportunity for developing new monitoring algorithms.This thesis proposes a voltage stability monitoring algorithm based on the synchrophasor-based linear state estimation method.Particularly, the voltage monitoring algorithm combines a set of early warning indicators with the BDS independence test (a statistical hypothesis test, after the initials of W. A. Brock, W. Dechert and J. Scheinkman).The early warning indicators are derived based on critical slowing down phenomenon in dynamical systems, and the BDS test serves as a diagnostic test to avoid false detections.The main advantage of the algorithm over other voltage stability indicators used in transmission side is that it can detect the onset of voltage instability in a faster and more accurate manner while avoiding false alarms when the system is still away from the stability boundary.Case studies conducted on a rural Quebec test feeder confirm the effectiveness of the proposed voltage stability monitoring algorithm.Reliable and fast detection of the proximity of the system states to voltage collapse conditions is achieved without false alarms.Furthermore, I am truly grateful to my co-supervisor Professor Gza Jos.His expertise in both academic research and practical industry, along with his insightful feedback and comments of my progresses helped me gain a better and deeper understanding of the research topic.Special thanks goes to Dmitry Rimirov, for the encouragements and enlightening discussions regarding
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How this classification was reachedexpand
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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 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 | 0.001 | 0.001 |
| 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".