Contingency screening for steady-state security analysis by using FFT and artificial 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
A new approach based on artificial neural networks (ANNs) combined with fast Fourier transform (FFT) is developed for single line contingency screening in steady-state security analysis. The offline fast decoupled load flow calculations are adopted to construct two kinds of performance indices, PI/sub p/ (active power performance index) and PI/sub v/ (reactive power performance index) which reflect the severity degree of contingencies. The results from offline calculations of the load flow are used to train a multilayered artificial neural network for estimating the performance indices. FFT is used for preprocessing the inputs to improve and speed up the ANN training. The effectiveness of the proposed method is demonstrated by contingency ranking on two IEEE test systems and comparisons are made with the traditional method. Good calculation accuracy, high contingency capturing rate and faster analysis times for contingency screening are obtained by using the ANNs.
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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 | 0.001 |
| 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) | 0.000 | 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 it