DSP-based fault detection for DC-DC converters
Bibliographic record
Abstract
Power electronics converter systems (PECS) are significant devices, which are used in industrial applications, including smart grids and variable speed AC drives. Therefore, the knowledge about the fault mode behavior of a converter system is extremely important from the perspective of protection and fault control in power systems. The present paper describes a novel on-line digital signal processor (DSP)-based diagnostic algorithm allowing the real-time detection, classification and localization of open-circuit (O-C) faults in the PECSs, as well as the identification of the unbalance input voltage to the converter. The proposed method requires much fewer input signals in comparison with the previous research works; therefore, the method avoids the use of additional sensors and signal processing devices that is important for the typical small-size commercial power converters used in distributed generation applications. Experimental results are presented using a coupled DC motor with AC synchronous generator scheme connected to the input of the power converter. The experimental results demonstrate the effectiveness and the robustness of the proposed diagnostic algorithms in this paper.
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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) | 0.000 | 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 | 0.000 |
| Open science | 0.000 | 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".