Estimation of induction motor equivalent circuit parameters and losses from transient measurement
Bibliographic record
Abstract
Due to robustness and low-cost, Induction motors are among the most commonly utilized types of motors in industrial applications. The operation and efficiency of any induction motor can be predicted with reasonable accuracy by solving its equivalent circuit. However, the equivalent circuit parameters may differ from the measured one with aging and when the operating conditions varies. So, it would be advantageous, if the motor parameters can be estimated by a simple and cost-effective method under running condition. Within this research, the circuit model parameters, motor losses, applied load torque and rotor inertia of a 3-phase induction motor at various loads have been estimated applying Particle Swarm Optimization (PSO) technique, from the measured transient current and supply voltage. Using the estimated quantities, various performance indicators were assessed. The predicted operational metrics were evaluated against the corresponding recorded experimental values. The comparison revealed negligible errors, establishing the reliability of the proposed method. In practical applications, the developed algorithm seems promising for predicting: (a) The control parameters associated with power electronic drives driving the induction motor. (b) The proposed parameter estimation technique, with appropriate modifications, could significantly contribute in the domain of fault classification for induction motors. (c) With the help of thermal models, this research work is capable of developing a temperature based predictive condition monitoring scheme for induction motors. (d) It has the potential to revolutionize the approach to motor monitoring, potentially enhancing operational efficiency, reliability, and lifespan.
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".