A Simulation-Based Classification Approach for Online Prediction of Generator Dynamic Behavior Under Multiple Large Disturbances
Bibliographic record
Abstract
This paper proposes a novel method for the machine learning-based online prediction of generator dynamic behavior in large interconnected power systems. Unlike the existing literature in this domain, which assumes faults occur immediately after a steady-state situation, the proposed method takes the possibility of multiple disturbances into account. It is founded on a simulation-based classification approach to indirectly take advantage of phasor measurement unit (PMU) data, which leads to improvements in robustness against load model uncertainties. Relying on offline scenarios, the method developed conducts multiple time-domain simulations (TDSs) in parallel for a set of feasible two-machine dynamic equivalent models (DEMs) for each case. Thereafter, common descriptive statistics are computed for the rotor angles obtained to form the feature space. The values taken via a feature selection process are then applied as inputs to ensemble decision trees, which train models capable of predicting both stability status and generator grouping ahead of time. In online situations, PMU data are used to create DEMs and the predictors are collected by performing parallel TDSs for DEMs. The functionality of the proposed hybrid machine learning and TDS-based approach is verified on several IEEE test systems, followed by a discussion of results.
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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) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 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".