Data‐Driven Methods in Modern Power System Stability and Security
Bibliographic record
Abstract
This chapter introduces a range of data-driven methods designed to maintain and enhance the stability and security of modern power systems. The integration of intermittent renewable energy sources and converter-interfaced generators introduces a high level of uncertainty and system model complexity, necessitating innovative approaches to address the challenges. Some methodologies presented in this chapter are developed by integrating stochastic dynamic system modeling and control theory with domain knowledge of physical power systems. These methodologies, utilizing wide-area monitoring system (WAMS) data, can be used online to estimate system modal properties, damp interarea oscillations, regulate wide-area voltage, and estimate the time-varying virtual inertia of converter-interfaced generators, despite topology change and model uncertainties. Additionally, another methodology is developed by leveraging advanced surrogate modeling and sparse regression technique. This methodology, exploiting raw data from intermittent renewable energy sources (e.g., wind speed and solar radiation), can accurately and efficiently assess the available transfer capability (ATC) of a transmission system and evaluate the ramping support capability (RSC) of a microgrid (MG). Collectively, these methodologies are envisioned to significantly enhance situational awareness and enhance the stability and security of modern power grids. Their integration of advanced mathematical tools with domain knowledge of power systems represents an important advancement in addressing the challenges introduced by the integration of renewable energy sources, contributing to the development of a more stable and secure power infrastructure.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.005 | 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".