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| Efficiency Optimization Method for Induction Motor Based on Attention Mechanism Physics-Informed Neural Networks |
| Wang Fengxiang1,2, Liu Yang1,2,3, He Long1, Liao Wendan1,2,3 |
1. National and Local Joint Engineering Research Center for Electrical Drives and Power Electronics Quanzhou Institute of Equipment Manufacturing Fujian Institute of Research on the Structure of Matter Chinese Academy of Science Quanzhou 362216 China;
2. Fujian College University of Chinese Academy of Sciences Fuzhou 350000 China;
3. College of Mechanical and Electrical Engineering Fujian Agriculture and Forestry University Fuzhou 350100 China |
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Abstract In industrial applications, induction motors are frequently selected with power ratings significantly higher than required to accommodate peak load demands. Consequently, these motors often operate under light-load conditions during normal operation, leading to low efficiency and substantial energy waste. Improving the operational efficiency of induction motors is critical for energy conservation. Traditional efficiency optimization methods face distinct limitations: loss model control（LMC）relies heavily on accurate motor parameters, which are prone to variation during operation; search control（SC）typically suffers from slow convergence and torque ripples. Although data-driven methods based on deep learning have recently emerged, most existing approaches treat the motor system as a black box, ignoring underlying physical laws. However, the poor generalization capabilities and a lack of interpretability limit its practical application in high-performance drive systems. Therefore, this paper proposes an efficiency-optimization method that utilizes attention mechanism-based physics-informed neural networks for loss minimization control（AM-PINNs-LMC). By integrating physical prior knowledge into the neural network architecture, this method achieves accurate prediction of the optimal d-axis current to minimize loss.
Firstly, a mathematical model of the induction motor under steady-state conditions that incorporates iron losses is established. The relationship between power loss and state variables is analyzed to derive physical prior knowledge, specifically the partial derivative of power loss with respect to the d-axis current. These partial derivatives serve as critical input features. Secondly, the AM-PINNs-LMC network architecture is constructed. A physics-guided single-head attention mechanism is designed and inserted after the input layer. Unlike traditional black-box attention mechanisms, this module dynamically assigns weights to input features, including q-axis current, mechanical angular velocity, and the calculated partial derivatives, based on physical importance scores derived from the loss gradients. Thus, the model focuses on features with clear physical significance, thereby enhancing prediction accuracy and model interpretability. Thirdly, a multi-objective loss function is formulated to supervise training, comprising data loss, physical-consistency loss, attention-supervision loss, and regularization terms. By minimizing the loss function, the network is constrained to converge towards solutions that satisfy both the empirical data patterns and the governing physical laws of the induction motor. Finally, the trained model outputs the optimal d-axis current, which is then transmitted to the control loop as a reference to minimize system losses.
Experimental results on a 0.75 kW induction motor platform validate the effectiveness of the proposed method. In terms of prediction accuracy, the mean squared error（MSE）of the AM-PINNs-LMC model on the test set is reduced to 9×10-5, representing 61.9% and 53.61% decreases compared to the baseline PINNs model without attention and the black-box attention model, respectively. In efficiency optimization tests conducted at 300 r/min under 5% rated load, the proposed method reduces input power by 11.5 W compared to traditional PI control, improving efficiency by approximately 21.49%. Compared to the LMC method, it reduces input power by 1 W, improving efficiency by approximately 3.05%. Furthermore, dynamic experiments at 600 r/min with a sudden load step from 7.2% to 15.8% of the rated load show that the proposed method reduces the steady-state current ripple to 0.08 A, compared with 0.17 A using the LMC method. The algorithm achieves an average execution time of 47 μs, meeting the requirements for real-time control.
The following conclusions can be drawn.（1）Embedding physical laws into the network structure significantly improves model generalization and reduces data dependence.（2）The physics-guided attention mechanism effectively enhances prediction accuracy by focusing on critical physical features.（3）The proposed AM-PINNs-LMC method achieves superior efficiency optimization and dynamic robustness compared to traditional PI and LMC methods.
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Received: 10 September 2025
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2026, Vol. 41 
