Multi-contingency transient stability-constrained optimal power flow using multilayer feedforward neural networks
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Transient stability-constrained optimal power flow (TSC-OPF) aims at optimising the scheduling of generation with stability constraints to ensure a secure system in the event of contingencies. This paper proposes a new approach based on a critical clearing time (CCT) constraint that replaces the dynamic and transient stability constraints of the TSC-OPF problem. The CCT is computed by a multilayer feedforward neural network (MFNN) trained using Gauss-Newton approximation for Bayesian regularization. In order to ensure a uniform distribution of generated points in the input space to train the neural networks, a Sobol quasi-random sequence is adopted for data generation. The proposed method has the merit of removing the computational burden of dynamic simulation during optimisation. Multi-contingency can simply be handled by adding a CCT constraint for each contingency. Simulation results for the New England 10-machine 39-bus system show that TSC-OPF using MFNN has very fast convergence to optimal operating points with the desired CCT.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 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 it