An Adaptive Fuzzy Mho Relay for Phase Backup Protection With Infeed From STATCOM
Bibliographic record
Abstract
This paper presents a fuzzy-logic-based scheme for the operation of generator-phase backup distance protection, otherwise popularly known as the “phase 21” function, in the presence of a STATCOM installed at the generator bus. The presence of STATCOM impacts the normal functioning of the distance relay based on its location as well as that of the fault. The method introduced here counters its adverse effects by formulating an adaptive mho relay for the phase 21 function, which accounts for the fast and dynamic compensation provided by the STATCOM throughout the operating time domain. Fuzzy logic is used in this paper to handle this varied compensation with its capability to process uncertain variation using linguistic variables to good effect. Two particular feature inputs from the STATCOM which have a direct impact on the reach of the relay are considered as the fuzzy system inputs. The objective is to counter and minimize the effect of current infeed from the STATCOM, on the apparent reach observed by the phase 21 relay and, thus, achieve the desired coordination. Electromagnetic transient simulations were used for the studies. The interaction between the simulation and the fuzzy system is performed online to further enable a closed-loop approach. Further furnished results validate the same interaction.
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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 | 0.000 |
| 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 | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".