Real-Time Implementation of IPM Motor Protection Using Artificial Neural Network
Bibliographic record
Abstract
This paper presents an on-line protection scheme for three-phase interior permanent magnet (IPM) motors using artificial neural network. The proposed protection scheme is developed and implemented in real-time using the DS1102 digital signal processor (DSP) board. In this work, a two-layer feed-forward neural network (FFNN) with sixteen inputs and single output is designed and trained off-line with experimental data using the back-propagation algorithm. An experimental setup is developed to accommodate the on-line testing and to carry out the protection of IPM motors. 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. The technique is evaluated and tested on-line on the laboratory 1-hp and 5-hp IPM motors using the DSP board. The laboratory results show that the proposed technique is able to detect the faulted conditions with high accuracy.
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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".