Developing and testing phaselet frames-based digital protection for distributed generation units
Bibliographic record
Abstract
This paper presents the development and performance testing of a new digital protection for interconnected distributed generation units (DGUs). The developed digital protection is structured to detect and respond to transient disturbances (fault and non-fault conditions) based on the magnitudes and phases of the high frequency sub-band contents extracted from the d-q axis components of the currents flowing through the point-of-common-coupling (PCC). These magnitudes and phases are extracted by employing a set of 6 phaselet frames. The employed phaselet frames are realized by a modulated filter bank that is composed of 6 digital high pass filters (HPFs). The coefficients of digital HPFs are determined by bi-orthogonal phaselet basis functions. Extracted magnitudes and phases of the high frequency sub-band contents of the PCC d-q axis current components provide signature information for accurate detection and identification of faults. The performance of phaselet frames-based digital protection is experimentally tested for two wind energy conversion systems and a photovoltaic system under different fault and non-fault conditions. Test results of the proposed digital protection demonstrate reliable and timely responses, along with negligible sensitivity to the type and control of DGUs, type and location of faults, and loading levels.
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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 | 0.001 | 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 | 0.001 |
| 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".