Acquisition and communication system for condition data of transmission line of smart distribution network
Bibliographic record
Abstract
Traditional MESH-based high-voltage transmission line condition data acquisition and communication systems collect all types of transmission line related condition data using the wireless monitoring device, and transmit condition data to the information center point through the wireless mesh node by wireless multi-hopping. The traditional methods are easy to generate lagging response and the high energy consumption, which result in high system condition data loss rate and low comprehensive utilization value. Therefore, smart distribution network transmission line condition data acquisition and communication system is designed based on the overall structure of the system, including data acquisition module, data communication module, transmission line condition monitoring communication module, and wireless transmission module of transmission line condition data. Tension, ambient temperature, solar radiation temperature, and wind direction signals collected by the data acquisition module are transmitted to the data communication module. After the collected signals are packaged to wake up G24, and establish a good GPRS network connection for data transmission. The transmission line condition monitoring communication module adopts an embedded operating system, which can combine its own functions to cut down the operating system, to speed up the response to the interruption event. The MCU in the transmission line condition data acquisition and communication system of smart distribution network realizes the command control of G24 by sending AT commands through the UART port. Data exchange between terminal and master station and addition of data items ensure the normal and smooth data communication. The experimental results show that the designed system can significantly reduce the loss rate of transmission line condition data and improve the system’s comprehensive utilization capability.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.009 | 0.003 |
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".