Performance of insulators under variation of pollution, inclined angle, and temperature based on the design of experiment
Bibliographic record
Abstract
The environmental conditions significantly impact the performance of the transmission line insulators. The current work investigated some variables that influence the flashover and breakdown voltages, such as insulator pollution ( P ), insulator inclined angles with the cross-arm ( A ), and insulator temperature ( T ). A prediction equation can be developed using Box-Behnken Design (BBD) to relate the input variables ( P , A , and T ) and the flashover voltage ( V FO ). BBD is a rotatable second-order design requiring three levels of each variable, influencing a specific experiment response. A statistical analysis was carried out to identify the significant variables affecting voltage V FO . So, the main objective of the work is to investigate the effect of P , A , and T on the V FO and use Box Behnken Design (BBD) to predict the value of V FO when the information about P , A , and T is available without requiring any experimental works. The results illustrated a prediction equation that facilitates the prediction of the voltage V FO if new values of the input parameters are known. The prediction error of the constructed equation was less than 5 % for a total number of 21 new data samples used to test and verify the robustness of the prediction equation. • The article focus on the impact of insulator's pollution, its inclined angle, and its temperature on the flashover voltage. • The article established a prediction equation based on BBD to link between these variables and the flashover voltage. • Minimum errors will be observed between the results of BBD prediction equation and that from the experimental works.
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 | 0.005 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| 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".