Online Application of Local OOS Protection and Graph Theory for Controlled Islanding
Bibliographic record
Abstract
Despite layers of protection at work, power systems have experienced major blackouts in the past due to cascading outages. Such outages can be prevented with controlled islanding, which consists of two problems: “when” and “where” to island. The majority of the existing offline and real-time methods for the “when” aspects are based on wide area measurement systems (WAMSs), which depend on the system topology to perform periodic offline simulations or real-time computation of dynamic system equivalents. In this paper, a simplified “when” approach is proposed based on local generator out-of-step (OOS) protection and generator coherency using fault location. The novelty of the work lies in the use of local OOS protection relays to identify the overall system instability, which eliminates the need for WAMS communication and the complex computation required in real-time methods to form dynamic system equivalents. As this approach does not require any equivalent networks, the methodology is independent of system topology. Unlike existing WAMS-based methods, the proposed methodology simplifies the communication between the central “when” unit and each generator's OOS protection relay using status flags communicated with the IEC 61850 R-GOOSE protocol. The proposed “when” methodology is combined with the “where” method based on graph theory to test the overall controlled islanding scheme in the IEEE 39-bus system using the real-time digital simulator (RTDS).
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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 | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".