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 paper 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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| 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) | 0.000 | 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 teacher head, 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".