A fuzzy logic application to represent load sensitivity to voltage sags
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
This paper presents a case study application of fuzzy logic in a power quality issue. It describes the computer-based load sensitivity to voltage sags, by using fuzzy sets and IF-THEN inference rules. The load sensitivity is based on the steady-state and transient voltage versus time profile according to the IEEE Std. 446, also referred to as the CBEMA (Computer Based Equipment Manufacturer Association) curve. Fuzzy logic allows the modeling of the inherent uncertainty of the load reliability. This expresses how the success or failure of computer based loads is correlated with short term voltage variations in the electric supply system. A fuzzy inference system is experimentally implemented for these cases, showing the general procedures of how to use this theory. It appears that fuzzy set theory can play an important role in diagnosing power quality disturbances, and hence it can offer insights towards the satisfaction of the needs of manufacturers, utilities and customers.
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.
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 | 0.003 |
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 it