Comprehensive analysis on three-phase imbalance management technology of low-voltage distribution network
Bibliographic record
Abstract
Energy production and use is one of the major sources of global greenhouse gas emissions. To combat climate change, many countries and organizations are pushing to reduce the use of fossil fuels, and the use of renewable energy sources such as solar, wind, and hydro is growing rapidly. The conversion of these renewable energy into electricity requires a large amount of power electronics. Power electronics have higher energy conversion efficiency, greater adjustability and control, and smaller size and weight than traditional generators. These characteristics make power electronic equipment have a wide range of application prospects in the field of energy conversion and power control. There may be cost, reliability, debugging, and three-phase imbalance issues. Based on the harm of three-phase imbalance to the economic operation and safe and stable operation of distribution network, the importance of three-phase imbalance in low-voltage distribution network is expounded, and the relationship between three-phase unbalance current and power factor is elaborated on the construction of three-phase imbalance. Reduce the harm of three-phase imbalance to the distribution network and reduce the loss of distribution network lines.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".