Grid Mirror: Harnessing Adversarial PINNs to Model Power Grid Dynamics
Bibliographic record
Abstract
This paper presents a novel approach to estimate the dynamic behavior of power grids using Physics-Informed Neural Networks (PINNs) from an adversarial perspective. The methodology involves injecting a load perturbation into the grid to induce a response from the generators to estimate the grid's dynamic response. The perturbations and generator frequencies are used to train a PINN, to estimate the grid's state-space model by including it in the PINN's loss function. By integrating physics constraints, the PINN ensures accurate and physically consistent estimations of grid dynamics. The PINN is initialized through educated guesses of the unknown matrices to ensure faster and more accurate conversion. The PINN demonstrates great potential with the estimated system states and generator frequencies closely following their actual counterparts with a correlation coefficient greater than 0.99. The achieved Relative Root Mean Square Error is under in the order of$\mathbf{1} \boldsymbol{\times} \mathbf{1 0}^{\mathbf{- 4}}$.
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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 | 0.001 | 0.005 |
| 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 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| 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".