Maximizing Synchronous Condensers' Capability to Stabilize Inverter-Based-Resource-Penetrated Grids
Bibliographic record
Abstract
Synchronous condensers (SynCons) have been deployed in power grids penetrated by inverter-based resources (IBRs) worldwide to strengthen and stabilize the grids. This paper examines which machine parameters influence IBR weak grid stability and whether excitation systems also play a role. Four types of stability scenarios are examined, including transient stability, oscillations of a few Hz, oscillations near 9 Hz, and dynamic voltage stability. It is shown that the most influential machine parameter varies for the different types of stability issues. While minimization of field winding inductance (typically the major component of the machine transient reactance,$X^{\prime }_{d}$) can significantly improve transient stability, voltage stability, and low-frequency oscillatory stability, this parameter has no influence on relatively rapid oscillations. On the other hand, minimizing rotor damper winding inductance (typically the major component of the machine subtransient reactance,$X^{\prime \prime }_{d}$) improves the 9-Hz oscillation stability, but with insignificant influence on the other three types of stability. Furthermore, the excitation system characteristics show negligible influence for any of the scenarios. In addition to the simulation studies, we show how the operational reactances are associated with the machine's$dq$impedance viewed from the terminal bus and how a SynCon reduces the equivalent grid impedance, thereby improving weak grid stability. Finally, it is concluded that minimization of both transient and subtransient direct-axis reactances should help in a range of stability scenarios, while cautions should be taken when dealing with quadrature-axis transient reactances.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".