A Dual Purpose Triangular Neural Network Based Module for Monitoring and Protection in Bi-Directional Off-Board Level-3 Charging of EV/PHEV
Bibliographic record
Abstract
Understanding the need for improvement in monitoring and protection in high performance charging technology for a growing demand of EVs/PHEVs, this research manuscript presents a part of an ongoing project and proposes a novel low cost dual purpose triangular neural network based module for power quality monitoring and protection (M&P) and elicits its performance in times of abnormalities or malfunction in a high performance off-board level 3 bi-directional charger for electric vehicles. Firstly, design and implementation of the low cost dual purpose triangular neural network based device for monitoring the power quality and hence, protecting the grid has been explained and its performance has been presented through numerical investigations. Going a step further, the device has also been experimentally tested using an in-house electric vehicle containing a commercially available battery charger and the measured results are analyzed. Secondly, a high-performance vector-controlled bi-directional off-board level-3 charger for faster and efficient charging has been developed and investigations have been performed on the healthy charger to analyze its performance. The primary aim of developing this charger was to elicit the usage and performance of the previously developed M&P device to protect the grid in case of some typical charger malfunction problem in such a charger, which is not detectable by conventional low cost sensors employed with such chargers. Once the module detects any abnormalities in the charger's operation, information gathered can be used to tune the controller in the charger to obtain a constant improved performance of the charger or the power transfer can be terminated.
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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 | 0.000 |
| 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) | 0.000 | 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".