Modelling, simulation, control, and enhanced topologies for the modular multilevel converter for high-voltage DC (HVDC) applications
Bibliographic record
Abstract
This thesis is focused on the modular multilevel converter (MMC) for High-Voltage DC (HVDC) systems. It is an attempt to address the issues associated with the modelling, simulation, control, efficiency, and fault-handling capability of the MMC. Thus, to address the modelling of the MMC, a new and more accurate steady-state harmonic model is proposed. The proposed harmonic model is capable of predicting the amplitude of the harmonic components of the MMC arm voltages, submodule capacitor voltages, and arm currents. Further, based on the proposed harmonic model, a capacitor sizing method is proposed to determine the capacitance of the submodule capacitor for a desired level of voltage variation, without a need for numerical algorithms or graphs used by the existing methods. In addition, the proposed capacitor sizing method can accurately determine the required capacitance even if circulating currents are injected to mitigate dc voltage fluctuations. The thesis also proposes a simple equivalent-circuit-based simulation model for MMC-based HVDC systems, which assumes ideal submodule switches to speed up the simulation, but is nonetheless capable of capturing the transients as well as harmonic components of the voltages and currents. Further, the thesis proposes a simple compensation strategy that calculates the magnitude of the second harmonic component of an arm voltage, and uses the calculated value as a feedforward signal to cancel the circulating current of the corresponding MMC leg. The proposed feedforward compensation strategy, if combined with a closed-loop circulating current suppression strategy, greatly mitigates the possibility of control saturation and, also, results in better damped closed-loop dynamics. Finally, the thesis proposes two new MMC topologies for enhanced efficiency and dc-side fault handling capability. In the first proposed topology, that is the lattice modular multilevel converter (LMMC), the entire MMC arm is modified to accommodate networks that allow shortcuts between the arm capacitors, thus, reducing conduction power losses of the converter. In the second topology proposed, however, only the submodule is modified. In the proposed submodule topology, referred to as lattice submodule (LSM), the conduction power losses are decreased, as it is the case for the LMMC, with the difference that the voltage stress in the switches are also reduced. Keywords: Control, lattice modular multilevel converter, lattice submodule, modelling, modular multilevel converter, simulation model.
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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".