Space Vector Modulation of Multi-level and Multi-module Converters for High Power Applications
Bibliographic record
Abstract
This thesis presents and investigates Space Vector Modulation (SVM) switching strategies for (i) a multi-level Diode-Clamped Converter (DCC) and (ii) a multi-module Voltage-Sourced Converter (VSC) system in which each module is a conventional two-level VSC. Although the SVM strategies are general and applicable for n-level DCC and n-module VSC systems, this text only concentrates on five-level DCC and four-module VSC systems. \n \nFor a five-level DCC, a computationally efficient SVM algorithm is proposed. The algorithm, that is based on a classifier Neural Network (NN), reduces the computational time for the SVM realization. Therefore, adequate saving of processor execution time, in each sampling period of SVM, is provided to carry out other functions, e.g. the calculations required for DC-capacitor voltage balancing task. The thesis also proposes a DC-capacitor voltage \nbalancing strategy to counteract the voltage drift phenomenon of (i) a passive-front-end five-level DCC, and (ii) a back-to-back \nconnected five-level DCC system. The proposed balancing strategy, that is based on augmenting the proposed SVM algorithm, takes advantage of the redundant switching states to minimize a quadratic cost function associated with voltage deviations of the \nDC-capacitors. The salient features of the proposed balancing strategy are (i) online calculation of SVM to select the best \nswitching states, (ii) minimization of switching frequency, (iii) minimization of the THD content of the AC-side voltage, and (iv) no requirement for additional power circuitry. \n \nFor a four-module VSC system a sequential sampling SVM strategy is proposed. The proposed strategy (i) provides harmonic cancellation/minimization at the net AC-side voltage of the multi-module VSC system, and (ii) offers a low switching frequency \nfor each VSC module. \n \nTechnical feasibility of the proposed SVM strategies for a five-level DCC and a four-module VSC system, as a STATCOM and a \nback-to-back HVDC system, are investigated and presented. The studies are conducted in the time-domain, in the PSCAD/EMTDC \nsoftware environment.
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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".