Fault Diagnoses for Industrial Grid-Connected Converters in the Power Distribution Systems
Bibliographic record
Abstract
The reliability of power electronics converter systems (PECSs) is of paramount importance in industrial, commercial, aerospace, and military applications. Therefore, the knowledge about the fault-mode behavior of a converter system is extremely important from the perspective of improved system design, protection, and fault-tolerant control. Faults of power switches in PECSs are classified as short circuit (S-C) faults, open circuit (O-C) faults, and degradation faults. S-C faults in most cases cause an overcurrent condition that is readily detected and acted upon by standard protection systems such as overcurrent, undervoltage, or overvoltage protection. However, the degradation faults and O-C faults often do not trigger fault protection but rather cause system malfunction or performance degradation. Since the standard protection system may not detect these fault types, their diagnoses become critical for PECSs. This paper presents new methods for fault detection, localization, and diagnosis for grid-connected power converters and the identification of the unbalance input voltage to the converter. The proposed fault diagnostic algorithms are verified in both the simulation and the experimental environments in order to evaluate their robustness and effectiveness. The power converter under the study consists of three main subsystems: the three-phase uncontrolled rectifier, the boost chopper, and the single-phase inverter circuits.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 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".