High-power modular multilevel converters: Modeling, modulation and control
Bibliographic record
Abstract
THIS dissertation addresses the technical challenges associated with the operation and control of high-power modular multilevel converters. To improve the performance of modular multilevel converter (MMC), a generalized three-phase mathematical model with common-mode voltage (CMV) is proposed. By using the proposed mathematical model, the magnitude of circulating currents, capacitors voltage ripple, and the ripple in DC-link current during balanced and unbalanced operating conditions can be minimized. The modulation scheme and switching frequency are directly affected the output power quality and the performance of the converter and control method. In this dissertation, a novel sampled average and space vector modulation scheme is proposed. These modulation schemes are suitable to control the MMC with any number of submodules (without modifications), operates at low switching frequency, minimizes the ripple in output current and voltage harmonic distortion, and reduces the output filter size. For reliable operation of MMC, the voltage balancing among submodules is mandatory. This dissertation proposes a generalized single-stage balancing approach with reduced current sensors to control the MMC. The proposed balancing approach is suitable to implement with both phase-shifted and level-shifted pulse width modulation schemes. With the proposed approach, it is also possible to control the MMC with half-bridge and three level flying capacitor submodules. Also, an improved balancing approach often referred as the dual-stage balancing approach is proposed to minimize the voltage harmonic distortion and device power losses. This dissertation also proposes a direct model predictive control (D-MPC) approach to minimize the ripple in submodule capacitors voltage. To implement D-MPC approach, a discrete-time model of MMC with CMV is proposed. With the use of proposed model, the D-MPC approach does not require a cost function to minimize the circulating currents. The computational complexity is one of the major issues in the implementation of D-MPC approach for MMC. In this dissertation, a novel reduced computational MPC approaches named as dual-stage D-MPC and indirect model predictive control (I-MPC) approach are proposed. These approaches significantly minimize the computational complexity and, improve the voltage and current waveform quality while operating at the low switching frequency. Finally, the simulation and experimental studies are presented to validate the dynamic and steady-state performance of proposed methodologies. Index Terms • Modular Multilevel Converters. • Capacitors Voltage Balancing. • Pulse Width Modulation Schemes. • Circulating Currents. • Capacitors Voltage Ripple • Direct Model Predictive Control. • Dual-Stage Direct Model Predictive Control. • Indirect Model Predictive Control. • Total Harmonic Distortion.
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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".