Stator Short-Circuit Fault Detection and Location Methods for Brushless DFIMs Using Nested-Loop Rotor Slot Harmonics
Bibliographic record
Abstract
This article proposes and demonstrates a fault-detection method to locate interturn short circuits (ITSCs) in the stator windings of a brushless doubly fed induction machine (BDFIM). The detection of ITSC is important in machine health prognostics as ITSC is an early stage fault that may lead to other faults such as coil-to-coil and coil-to-ground. BDFIM consists of power and control windings in the stator that are magnetically coupled through a nested-loop rotor winding. Existing ITSC detection algorithms use rotor slot harmonics in stator current spectra as fault indicators for only conventional doubly fed induction machines. However, these algorithms cannot be applied to BDFIM due to its different stator/rotor winding structure. This article primarily proposes a new analytical formulation for the nested-loop rotor slot harmonics (NRSHs) as ITSC fault indicators in BDFIMs. Using NRSHs, a detection algorithm is also proposed to detect and locate ITSC in power/control windings of BDFIMs. The proposed algorithm is verified based on numerical analysis of a BDFIM using the finite-element method. The accuracy of the proposed fault-detection algorithm is also experimentally investigated and demonstrated using a BDFIM test rig. Both numerical and experimental test results confirm effectiveness of the proposed ITSC fault-detection algorithm.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| 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".