ArticleScientific reports2026
In-situ efficiency and parameter estimation for induction motors using heuristic optimization.
Article in Scientific reports, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
0 citing papers in PubMed.
No citing paper in PubMed yet.
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
3 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Electric motors, especially three-phase induction motors, consume a significant portion of industrial electricity, making their efficiency critical for energy conservation. Traditional efficiency assessments require intrusive, labor-intensive laboratory tests, limiting in-situ evaluations. We propose a non-intrusive, heuristic optimization-based method compliant with IEEE Std-112 A/F1 to simultaneously estimate equivalent circuit parameters and in-situ efficiency using only field-measurable electrical quantities. Eight meta-heuristic algorithms were applied across six motors of varying power ratings and load conditions, with results validated against standard test data. The method achieved mean absolute errors below 0.7% at full load, demonstrating high accuracy and stability. This approach enables reliable, low-cost, and continuous efficiency monitoring under real operating conditions, offering practical benefits for industrial energy management and motor performance assessment.
Indexed as
Identifiers
What OpenQuestion holds
Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.