Improving apparent inertia, damping, stability, and fault recovery performance in AC power networks with virtual synchronous machines
Bibliographic record
Abstract
Traditional VSC control is of the “Grid Following Type” (GFL), where the turn-on pulses to the converter switches are determined based on fast synchronization with the external grid voltage phasor. GFL VSC has difficulty operating in very weak ac networks. In contrast, GFM-controlled VSC maintains an internal voltage phasor and has the potential to maintain system stability under such challenging network conditions. One important aspect of VSM type control of a VSC is that it imparts artificial inertia and damping behaviour to the VSC, which can have a significant impact on the VSMs ability to survive frequency events triggered by transient mismatch between generation and loads. This research investigates the disturbance ride through, stability and inertia support of using Virtual Synchronous machine (VSM) Grid-Forming (GFM) control on Voltage-Sourced Converters (VSC) High Voltage direct current (HVdc) transmission system. An adaptive fault ride-through control mode is proposed for VSM to improve its post-fault recovery transient and voltage phase angle jump ride-through performance. The EMT simulation shows the disturbance ride-through performance of VSM GFM with cascaded or switchable current control can be optimized by transiently changing the active power order and the damping coefficient in its power synchronization loop. Linearized models of the VSM with inertia and damping parameters are developed and validated using detailed EMT simulation for system small-signal stability analysis and controller parameter design under different system strengths. A current limiting feature is a necessity in any VSC due to the limited overcurrent ratings of the VSC’s power electronic switches. However, eigenvalue analysis reveals that the VSM can experience instability when connected to strong ac networks with the inclusion of the in-line cascaded current limiting. However, with proper controller tuning, a single suitable gain parameter set can nevertheless be found to allow stable operation under both weak and strong system strength. An important contribution of this research is the development of a novel method for online estimation of system inertia in inverter-based resources (IBRs). A small voltage or current probing signal that is different from the system frequency can be injected into a network to estimate the inertia from the GFM VSCs and synchronous machines. Since the injected signal is very small, the inertia of the GFM IBR can be conveniently measured during the steady-state operation without triggering other transient frequency support control. This can greatly improve the inertia measurement accuracy compared with the more conventional ROCOF observation method.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 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 | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".