Communication networks and non-technical energy loss control system for smart grid networks
Bibliographic record
Abstract
In smart grid networks, the traditional electrical networks are automated using sensors and control algorithms, where the control information flows in a communication network to the control center. The control information is delay sensitive and requires a reliable communication. Hence, it is necessary to select the best communication technology from the available candidates to satisfy the high Quality of Service (QoS) requirement of control information. Among various applications involved in a smart grid network, providing solution to control non-technical losses including electrical theft is one of the most serious problems in developing countries. Therefore, this paper discusses the suitable communication networks and proposes a framework to control non-technical losses in energy distribution systems. The non-technical losses in customer premises like tampering of electrical devices are conveyed, whereas an unauthorized theft in overhead lines is computed at the control center. Then, the control center identifies the electrical theft in a particular segment of the feeder and tries to identify the exact location using unmanned aerial vehicle (UAV). Finally, the control center finds the nearest staff personnel using Global Positioning Systems (GPS) and conveys power loss and theft details using General Packet Radio Service (GPRS) network to control the electrical theft.
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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 | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".