Robust Higher-Order Sliding Mode Control for Virtual Synchronous Generators to Enhance Stability Under Varying Network Conditions
Bibliographic record
Abstract
The Virtual Synchronous Generator (VSG) enables inverter-based resources (IBRs) to emulate the dynamic behavior of synchronous generators, enhancing grid-forming capability and power system stability. Conventional control strategies like Multi-Loop PI (ML PI) for cascaded voltage and current control, though widely used, rely on fixed gain parameters and lack adaptability across varying grid strengths, characterized by the Short Circuit Ratio (SCR). To overcome these limitations, this paper proposes a robust Higher Order Sliding Mode Control (HOSMC) strategy for cascaded voltage and current control in VSGs. HOSMC inherently provides variable gain, strong robustness to parameter uncertainties and disturbances, and finite-time convergence, without requiring gain retuning as grid conditions change. Moreover, it mitigates chattering through continuous control laws, addressing a key limitation of traditional SMC. Unlike most nonlinear controls, HOSMC allows frequency-domain stability analysis, simplifying controller design and assessment. The proposed approach is evaluated through small-signal stability analysis and electromagnetic transient (EMT) simulations across a wide range of SCR and X/R ratios. Results show that HOSMC achieves higher and more uniform stability margins than ML PI. A scenario involving dynamic SCR variation is simulated by switching a transmission line, with the VSG operating in the IEEE 118-bus system. The system remains stable throughout, demonstrating the control’s robustness. Real-time Controller Hardware-in-the-Loop (CHIL) validation confirms the practical feasibility of HOSMC under varying grid conditions, including SCR variations, voltage and frequency disturbances, and large faults.
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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".