Hysteresis control of voltage source converters for synchronous machine emulation
Bibliographic record
Abstract
Synchronous generators (SGs) are the major contributors in maintaining the stability of power systems. As SGs are displaced by electronically-coupled distributed energy resources (DERs), the dynamic behaviour of these new generation technologies will become critical in ensuring grid stability. One method of ensuring continued provision of voltage and frequency regulation is to have these new devices emulate the response of traditional SGs. Achieving the desired emulation behaviour requires fast, robust control of DERs to track the rapid changes in current injections that arise when SGs are subjecting to changing grid conditions. Hence, this paper focuses on the feasibility of controlling a voltage source converter to emulate the behaviour of a SG during both steady-state and transient periods. Two types of hysteresis current controllers (HCCs)-standard and space vector based-are considered for tracking the current injections of a virtual SG in real-time. Through simulation, it is demonstrated that both HCCs provide robust, fast tracking that enables the desired emulation, but they exhibit a wide range of variations in switching frequencies. Hence, this paper proposes a dead-time control method and demonstrates that the switching frequencies can be limited while maintaining the quality of voltages and currents that are compatible to the IEEE 1547 standard.
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) | 0.000 | 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) | 0.001 | 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".