Graphical Analysis and Control of Cascaded H-Bridge Multilevel Inverter Using Clarke Transformation With Neutral-Shift Strategy Under Nonideal Conditions
Bibliographic record
Abstract
This article proposes a graphical approach using a 3-D space vector representation to diagnose the unbalanced operation of a three-phase cascaded H-bridge (CHB) inverter operating under nonideal conditions. Under ideal conditions, the application of the Clarke transformation to the inverter voltage phasor results in a circular trajectory within the stationary and orthogonal ($\alpha \beta $)-frame, enabling the inverter to supply balanced three-phase phase/line voltages and currents to the load. In contrast, when nonideal conditions arise, such as dc voltage imbalances among the bridges or failed cells, the Clarke transformation of the inverter voltage phasor forms an elliptical path, leading to unbalanced operation. This article discusses and highlights the characteristic differences between inverter-balanced and unbalanced operation modes. Additionally, a generalized neutral-shift method has been suggested to address unequal dc voltage sources simultaneously and failed cells without differentiation. The proposed control strategy ensures the inverter maintains balanced line-to-line voltages and currents, even when its phase voltages remain unbalanced. Simulation and experimental results from a three-cell (per phase) CHB inverter with a symmetrical RL load are provided to validate the effectiveness of the proposed approach. Finally, the study explores the impact of nonideal conditions on modifying inverter voltage and current spectra.
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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".