Neuro-WPT Based Diagnosis and Protection of Three-Phase IPM Motors
Bibliographic record
Abstract
This paper presents the practical implementation of a novel fault diagnosis scheme for an interior permanent magnet (IPM) motor protection using the neural network (NN). The preprocessing of line currents of different faulted and normal unfaulted conditions of an IPM motor are carried out by the wavelet packet transform (WPT) in order to minimize the structure and the training time of the proposed neural network based protection technique. It has been shown that the wavelet packet transformed second level high frequency coefficients (dd <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sup> ) of line currents are able to differentiate between the healthy and faulted conditions. These are used as the input sets of a three-layer feed-forward neural network. The scheme is evaluated and tested on-line on a laboratory 1-hp IPM motor using the ds1102 digital signal processor board. Three types of faults such as single line to ground (L-G) fault, line-to-line (L-L) fault, and single phasing fault are investigated. In all the tests carried out, the types of fault are classified and identified promptly and properly, and the tripping action is initiated within one cycle of the fault occurrence based on a 60 Hz system.
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 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 | 0.000 |
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 teacher head, 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".