Fault Diagnosis of Cascaded H-Bridge Inverter Using Model Predictive Control
Bibliographic record
Abstract
This paper proposes a new fault detection and localization (FDL) algorithm based on modulated model predictive control (M2PC) method for Cascaded H-bridge (CHB) inverters. Model predictive control (MPC) algorithms have been widely applied in medium-voltage (MV) power converters for its advantages in dynamic performance and control of multiple objectives. However, it has some limits for multilevel topologies, including variable switching frequency and large computation burden. In addition, in cascaded H-bridge (CHB) multilevel inverters, fault detection and localization need to be incorporated into the control system to make it work normally when there is an open fault in power cells. This paper employs M2PC approach to calculate output voltage references with reduced computation burden in every sampling time. Also, M2PC features a fixed switching frequency in the system which helps improving harmonic spectrum and simplify filter design. Furthermore, FDL scheme can detect open switch fault in CHB multilevel inverter by comparing actual and predicted phase voltages. With the fault detection method, open switches can be localized through the fault matrix with the measurement of load currents and phase voltages in various open-fault scenarios. Simulation results verify the performance of proposed M2PC and FDL algorithms.
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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) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| 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".