A Computationally Efficient Method to Design Probabilistically Robust Wide-Area PSSs for Damping Inter-Area Oscillations in Wind-Integrated Power Systems
Bibliographic record
Abstract
This paper proposes an efficient method that tunes wide-area power system stabilizers (WPSSs) to have probabilistic robustness for damping inter-area electromechanical oscillations in power systems with incorporated random wind power. Specifically, the efficiency of this method benefits from unique consideration and deduction of the analytic forms of cumulative distribution functions (cdfs) of two types of random variables and their derivatives with respect to tunable parameters of the conventionally structured WPSSs, based on approximations of wind power probability density functions by Gaussian mixtures. These cdfs then compose the objective function of an optimization that can rapidly solve for optimal parameters of WPSSs by a sequential quadratic programming algorithm. The optimized WPSSs are probabilistically robust because they enhance the probability of two commonly desired control effects. Simulation studies on a modified IEEE 10-machine and 39-bus system validate the superior efficiency of the proposed tuning method and the excellent performance of the derived WPSSs.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".