Adaptive Inertia Estimation of a Power System Area Using Variable Forgetting Factor Recursive Least-Squares
Bibliographic record
Abstract
The increasing use of converter-interfaced generators (CIGs) in modern power grids has affected system inertia and posed challenges to grid stability. In this regard, accurate and real-time monitoring of inertia is crucial for maintaining system stability, especially in low-inertia grids where even small disturbances can lead to rapid frequency deviations. This paper proposes a novel approach for inertia estimation using a variable forgetting factor recursive least squares (VFF-RLS) algorithm, which dynamically adapts to time-varying conditions in power systems. By using ambient measurements provided by the widearea measurement system (WAMS), the proposed approach can capture inertia variations of areas in power systems. The method is validated through simulations on the IEEE 39-bus system, demonstrating higher accuracy compared to existing approaches under both time-constant and time-varying inertia conditions.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".