Power System Stabilizer Design Using an Online Adaptive Neurofuzzy Controller With Adaptive Input Link Weights
Bibliographic record
Abstract
A neurofuzzy controller (NFC) with adaptive input link weights (ILWs) and working as an adaptive power system stabilizer is presented. The control structure of the proposed adaptive neurofuzzy power system stabilizers (ANFPSSs) consists of a neuroidentifier to track the dynamic behavior of the plant and an NFC to damp the low-frequency power system oscillations. Usually, the input membership functions (IMFs) and consequent parameters (CPs) are adapted in order to enhance the performance of the NFC. However, the adjustment of IMFs can be realized indirectly by the tuning of ILWs introduced here, which is simpler due to the small number of parameters involved. Therefore, in this paper, ILWs and CPs are updated online by the gradient descent method. Simulation studies over a range of operating conditions and disturbances in a single machine-infinite bus system and a multimachine power system demonstrate the improvement in the dynamic performance of the system with the proposed ANFPSS.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| 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".