A new stationary frame multi-input multi-output EMT-level frequency scanning method for inverter based resources
Bibliographic record
Abstract
Impedance-based stability analysis (IBSA) is an effective method to identify subsynchronous interaction (SSI) problem between the inverter-based resources (IBRs) and series compensated or weakly tied AC grids. The electromagnetic transient (EMT) level positive sequence and dq-frame frequency scanning methods (p-scan and dq-scan, respectively) are used to obtain the sequence single-input single-output (SISO) and dq multi-input multi-output (MIMO) impedance of IBRs, respectively. The dq MIMO impedance usage in IBSA provides more accurate results compared to the sequence SISO impedance. This paper proposes an EMT-level αβ-frame frequency scanning method (αβ-scan) to obtain the αβ MIMO impedance and its usage in IBSA. The αβ-scan requires significantly less time compared to dq-scan for the convergence of MIMO IBR impedance representation and offers similar accuracy with dq-scan in IBSA. The accuracy of the proposed αβ MIMO IBSA is validated by comparing with dq MIMO IBSA and EMT simulations on series capacitor SSI cases with different type IBRs (Doubly-fed induction generator (DFIG)-based wind park (WP) and full-scale converter (FSC)-based WP). This paper also uses a multivariable structure function(MSF)-based method for the first time in IBSA of SSI to achieve Bode plot-based analysis for providing better visualized presentation of resonance frequency and stability margins compared to generalized Nyquist criterion.
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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 | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.004 | 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".