Phaselet Transform-Based Digital Ground Fault Protection of Grid-Connected Photovoltaic Systems
Bibliographic record
Abstract
The growing interests in utilizing Photovoltaic (PV) systems are usually faced with challenges of accurate and reliable protection of these distributed generation units. A desired protection of PV systems has to effectively and accurately detect and respond to internal faults (within the PV system) and external faults (in the host grid and/or fed loads). This article presents and tests the performance of a digital ground fault protection (DGFP) for grid-connected PV systems. The presented DGFP detects faults based on the energy contents of high frequency sub-bands of the ground current. These energy contents are extracted using the phaselet transform that can process signals with non-stationary variations in their phases. Energy contents of the high frequency sub-bands can provide accurate, fast, and reliable detection and identification of faults experienced by a PV system operated. The phaselet transform (PHT)-based DGFP is implemented for performance testing using PV systems that are operated in the grid-connected mode, and subjected to various fault and non-fault events. Performance results demonstrate accurate, fast, and reliable detection and response to different types of fault and non-fault events. Response features of the PHT-based DGFP are complimented with minor sensitivity to the level of power production and/or type and location of faults.
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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 | 0.000 | 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) | 0.001 | 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".