Design of a robust and practicable SSI damping controller using H <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML" display="inline" id="d1e238" altimg="si3.svg"> <mml:msup> <mml:mrow/> <mml:mrow> <mml:mi>∞</mml:mi> </mml:mrow> </mml:msup> </mml:math> technique for series compensated DFIG-based wind farms
Bibliographic record
Abstract
This paper designs a robust and practicable subsynchronous interaction (SSI) damping controller using H∞ technique for the safe operation of series capacitor compensated wind farms (WFs) with doubly-fed induction generator (DFIG) wind turbines (WTs). Mixed sensitivity control design together with pole placement are formulated into a set of linear matrix inequalities (LMIs) to obtain the controller parameters. The LMI technique allows to include both desirable frequency and time domain specifications. The proposed damping controller is integrated into the WT controller (WTC) and receives the DFIG converter currents as inputs. The implementation of the proposed controller does not require any communication links between the WTs and WF secondary control layer. The controller output signals are applied to the inner control loops of DFIG converters and are dynamically limited for the desired fault-ride-through (FRT) performance. The effectiveness of the damping controller is verified through detailed electromagnetic transient (EMT) simulations. In these simulations, the complete medium-voltage (MV) collector grid is modeled with all details, and it is assumed that the wind speed at the location of each turbine follows a Gaussian distribution. The collected results confirm the accuracy of the modeling of the entire WF as an aggregated WT with the average wind speed.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| 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) | 0.002 | 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".