Radial basis function based identifiers for adaptive PSSs in a multi-machine power system
Bibliographic record
Abstract
Effectiveness of self-tuning adaptive power system stabilizers (APSSs) has been demonstrated in the literature. A self-tuning control algorithm consists of an identifier and a controller. As the algorithmic based identifiers require a relatively long computation time, investigations are being conducted to replace these with neural network based identifiers. In an interconnected power system, the generating units have different characteristics and sizes necessitating different pre-trained identifiers. Data gathered at various operating conditions and disturbances are used to train the RBF identifiers. The standard procedure for obtaining the centers for RBF identifiers involves initialization of the centers to random points in the input space. The centers are then updated using recursive rules. One limitation of this procedure is that even though the RBF centers learn the distribution of the input vectors by minimizing the distance between input vectors and the centers, they take long training time or fail to learn the topology of the input vectors. To overcome this limitation, an algorithm that creates RBF centers one at a time is described in this paper. Simulation studies on a five machine power system illustrate the effectiveness of this approach to the design of APSSs.
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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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".