Power Swing in Systems With Inverter-Based Resources—Part I: Dynamic Model Development
Bibliographic record
Abstract
While power swing is a well-understood phenomenon in conventional power systems, the power swing characteristics of systems with inverter-based resources (IBRs) remain significantly under-theorized. This paper demonstrates that this knowledge gap carries practical ramifications, including the potential to undermine the stability and reliable protection of future grids, where swing dynamics can be heavily influenced by IBRs. The paper investigates power swing in systems with IBRs in two parts. Part I of the paper develops the necessary relations to devise a novel state-space model for systems with IBRs. This analytical model is necessary to (i) quantitatively identify the distinct characteristics of power swings in IBR-rich grids, and (ii) theoretically prove that these characteristics can be generalized. The paper highlights fundamental differences between this new model and the well-established model for power swings in systems that consist solely of synchronous machines (SMs). To reveal the features of power swing in systems with mixed generation types, the paper also systematically incorporates the dynamic equations of SMs into the developed model. The accuracy of the proposed model is evaluated against PSCAD/EMTDC simulation results for a benchmark test system that includes multiple IBR plants and their detailed control systems. Part II of the paper will build upon the findings from Part I to investigate the implications of the specific features of IBRs' power swing from the perspective of power system protection.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 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".