A Gramian Angular Summation Field-Convolutional Neural Network-Based Openswitch Fault Detection Technique for Interfacing Inverters in Microgrids
Bibliographic record
Abstract
This paper proposes a novel adaptive open-switch fault detection technique using Gramian Angular Summation Field (GASF) and Convolutional Neural Network (CNN) for renewable distributed generation (DG)'s interfacing inverters in microgrids. Since the inverter control is essential in microgrids, three different inverter control schemes (droop control, Virtual Synchronous Generator (VSG) control, and VSG with a Fuzzy secondary controller) are evaluated regarding their impacts on open-switch fault detection. This paper uses a novel dataset measured through Opal-RT real-time simulator in our lab for a three-phase voltage source inverter (VSI) in a microgrid under various healthy, single- and multi-open-switch fault conditions, where the three control schemes and different inverter loadings are also implemented simultaneously. The measured time series inverter three-phase currents serve as signals. These current signals are first converted into images using GASF, the images are then fed into the CNN model for fault classification. The proposed method can accurately detect the type and location of inverter open-switch faults under various control schemes and inverter loadings.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 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 | 0.001 | 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".