Commutation Failure Reduction in HVDC Systems Using Adaptive Fuzzy Logic Controller
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Bibliographic record
Abstract
The use of fuzzy logic controllers to improve the performance of HVDC systems under various faults and operating point changes has been well-documented. Recent research has shown that the optimal membership function width in a fuzzy controller is different for different system events (such as current order change or fault). This paper proposes an adaptive approach to designing a fuzzy logic controller for HVDC systems. Simulation data are used to create a look-up table for the fuzzy control system. Based on the operating point and the type and location of fault, the output of the table indicates the membership function width to be used in the fuzzy controller to obtain the best results (i.e., the lowest number of commutation failures). This paper presents the best controllers for nine faults at 27 operating points for the CIGRE benchmark HVDC system, but more importantly, uses trends to develop a method for finding these optimal controllers for other systems without the need for exhaustive simulation of each possibility.
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it