Treffer: Dataset for optimized design parameters of three-phase induction motors with validation through machine learning.

Title:
Dataset for optimized design parameters of three-phase induction motors with validation through machine learning.
Authors:
Potnuru UK; Department of Electrical and Electronics Engineering, GMR Institute of Technology, Rajam 532127, India., Teegala SK; Department of Electrical and Electronics Engineering, GMR Institute of Technology, Rajam 532127, India., Kalabarige LR; Department of Computer Science and Engineering, GMR Institute of Technology, Rajam 532127, India., Ippili V; CADFEM India Pvt. Ltd., Hyderabad 500082, India., Holla MR; Manipal Institute of Technology, Manipal Academy of Higher Education, Manipal 576104, India.
Source:
Data in brief [Data Brief] 2025 Nov 13; Vol. 63, pp. 112274. Date of Electronic Publication: 2025 Nov 13 (Print Publication: 2025).
Publication Type:
Journal Article
Language:
English
Journal Info:
Publisher: Elsevier B.V Country of Publication: Netherlands NLM ID: 101654995 Publication Model: eCollection Cited Medium: Internet ISSN: 2352-3409 (Electronic) Linking ISSN: 23523409 NLM ISO Abbreviation: Data Brief Subsets: PubMed not MEDLINE
Imprint Name(s):
Original Publication: [Amsterdam] : Elsevier B.V., [2014]-
Contributed Indexing:
Keywords: Design optimization; Design parameters; Performance characteristics, machine learning; Three phase induction motor
Entry Date(s):
Date Created: 20251211 Date Completed: 20251211 Latest Revision: 20251211
Update Code:
20251211
PubMed Central ID:
PMC12686921
DOI:
10.1016/j.dib.2025.112274
PMID:
41377186
Database:
MEDLINE

Weitere Informationen

Three-phase induction motors continue to dominate industrial and commercial sectors due to their high efficiency, robustness, and low maintenance requirements. This data article presents a curated dataset of optimized design parameters for three-phase induction motors covering output ratings from 0.5 kW to 100 kW. Motor parameters (stator/rotor dimensions, winding details, air-gap flux, copper/core losses, torque, slip, efficiency, power factor, currents, flux per pole, temperature rise, etc.) were computed by a Python based computational framework implementing standard electromechanical design equations. The original 200 design instances were scientifically expanded to 6000 to represent viable design alternatives. To demonstrate dataset reliability and practical utility, descriptive statistics and tree-based regressors (Decision Tree, Random Forest, Extra Trees) were applied on held out test sets and evaluated with MAE, RMSE, and R². The Extra Trees model yielded the lowest errors (e.g., MAE ≈ 7.31 W and RMSE ≈ 11.62 W for full-load losses; MAE ≈ 0.0073% and RMSE ≈ 0.0232% for efficiency) with R² ≳ 0.9996 and residuals concentrated near zero (≈0.0073-9.1536). These results confirm the internal consistency of the physics-driven dataset and its suitability for simulation, preliminary design studies, controller tuning, and predictive maintenance. However, the current dataset does not incorporate nonlinear magnetic effects, thermal constraints, or experimental validation, which will be addressed in future extended versions.
(© 2025 The Author(s).)