Model‐based tuning approach for multi‐band power system stabilisers PSS4B using an improved modal performance index
Bibliographic record
Abstract
Multi‐band power system stabilisers (MB‐PSSs) PSS4B (IEEE standard 421.5‐2005) are advanced power system damping controllers that have evident advantages over conventional PSSs in damping low‐frequency oscillatory modes. However, finding optimal settings for such controllers is challenging due to the increased complexity of the PSS4B structure. This study describes a methodology for MB‐PSS parameter optimisation based on an improved modal performance index as a measure of the controller's stabilising effect. The tuning problem is formulated as a non‐linear constrained optimisation search method: proposed modal performance index is chosen as an objective function to be minimised, while properly selected constraints ensure stability of the closed‐loop system and robustness of the proposed design. The methodology is demonstrated on a benchmark system that is based on an existing network. Comparative analysis between the MB‐PSSs with optimised settings and speed‐based PSS1A‐type stabilisers designed using the conventional methods show the practicality and effectiveness of the proposed methodology. The implemented approach has an advantage of being scalable and suitable for the model‐based tuning of feedback controller of general structure. Additionally, several performance metrics and non‐linear simulations in the ElectroMagnetic Transient Program (EMTP) software confirm superior characteristics of PSS4B.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| 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".