Coordinated Supplementary Damping Control of DFIG and PSS to Suppress Inter-Area Oscillations With Optimally Controlled Plant Dynamics
Bibliographic record
Abstract
This paper proposes a design method to coordinate dual-channel supplementary damping controllers (SDCs) of doubly-fed induction generators (DFIGs) and power system stabilizers (PSSs) for suppression of inter-area power oscillations. A dynamic performance index is introduced to measure the dynamics of the conventional synchronous generator and DFIG during the damping control process. Hence, the proposed method designing the PSS and SDC is formulated as an optimization problem with the objective function being the sum of weighted performance indexes and the constraints indicating the requirements on the damping of the inter-area modes. Solving the optimization problem can obtain the optimal SDC and PSS, which can meet the required damping results as well as optimize dynamics of the controlled plants. Moreover, by adjusting weights in the objective function, the damping control burden can be flexibly and feasibly allocated between active and reactive power channels of DFIGs or among the damping controllers. Simulations with the modified New England and New York interconnected system prove that the proposed optimization based tuning method can not only robustly coordinate the PSS and SDC to effectively damp inter-area oscillations but also improve the dynamics of controlled plants during the damping control process over different operating conditions.
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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 | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".