Lissajous and Quadratic Modeling for Unbalanced Operation Analysis in Multilevel H-Bridge Inverters for Large-Scale Solar Applications
Bibliographic record
Abstract
This paper presents a novel analytical framework for evaluating unbalanced operation in solar-fed multilevel Hbridge (CHB) inverters. A combined Lissajous-based and quadratic geometric modeling approach is proposed to characterize voltage imbalance and fault conditions under asymmetric DC source voltages typical of photovoltaic systems. The developed models analytically derive key ellipse parameters, including major and minor axes, eccentricity, and orientation, to quantitatively represent the degree and nature of voltage imbalance in the inverter. Furthermore, a rebalancing condition is established to facilitate compensation modulation and adaptive control, ensuring stable operation under unequal solar string voltages or partial shading conditions. The proposed modeling framework enables direct visualization of imbalance evolution in the$\boldsymbol{\alpha} \boldsymbol{\beta}$voltage plane and provides a diagnostic tool for condition monitoring. Comprehensive simulation and experimental validations on a three-cell-per-phase CHB inverter confirm the accuracy and effectiveness of the proposed Lissajous and quadratic models, demonstrating improved system reliability and operational resilience in large-scale solar power applications.
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 | 0.001 |
| Open science | 0.001 | 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".