Torque and Temperature Prediction for Permanent Magnet Synchronous Motor Using Neural Networks
05 Mar 2021-pp 1-6
TL;DR: In this paper, the authors developed a torque and stator temperature prediction model for permanent magnet synchronous motors using neural networks, which can predict torque and four other temperature parameters at the permanent magnet surface, stator's yoke, tooth, and winding.
Abstract: This paper focuses on developing a torque and stator temperature prediction model for permanent magnet synchronous motors using neural networks. The model can predict torque and four other temperature parameters at the permanent magnet surface, stator's yoke, tooth, and winding. The motor's torque and temperatures are predicted without installing any additional sensors into it. Using the training dataset with Levenberg-Marquardt optimization and Bayesian regularization algorithms, the predicted model has the best performance with the least mean square error and the best $R^{2}$ values. Also, the prediction of testing data shows that the estimated model follows closely with actual values. This is true for all the five output parameters.
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14 citations
01 Jul 2021
TL;DR: In this article, a prediction model was developed using a support vector machine (SVM) algorithm to predict heart stroke using the parameters, namely, age, hypertension, previous heart disease status, average body glucose level, BMI, and smoking status.
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11 citations
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8 citations
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8 citations
01 Jul 2021
TL;DR: In this article, the authors developed a prediction model for chaotic behavior in fractional-order Duffing's oscillator using neural networks, which predicts the change in state variables' values of the oscillator by numerically solving the governing equations using the famous Grunwald-Letnikov's approach.
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7 citations
References
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TL;DR: Both the simulation and the experimental results suggest that the MPC for the PMSM can improve the speed-tracking performance of the motor and that this motor has a fast speed response and small steady-state errors under the rated load.
Abstract: In this study, a current model predictive controller (MPC) is designed for a permanent magnet synchronous motor (PMSM) where the speed of the motor can be regulated precisely. First, the mathematical model, the specifications, and the drive topology of the PMSM are introduced, followed by an elaboration of the design of the MPC. The MPC is then used to predict the current in a discrete-time calculation. The phase current at the next sampling step can be estimated to compensate the current errors, thereby modifying the three-phase currents of the motor. Next, Simulink modeling of the MPC algorithm is given, with three-phase current waveforms compared when the motor is operated under the designed MPC and a traditional vector control for PMSM. Finally, the speed responses are measured when the motor is controlled by traditional control methods and the MPC approach under varied speed references and loads. In comparison with traditional controllers, both the simulation and the experimental results suggest that the MPC for the PMSM can improve the speed-tracking performance of the motor and that this motor has a fast speed response and small steady-state errors under the rated load.
18 citations
06 Sep 2019
TL;DR: The permanent-magnet synchronous machine (PMSM) drive is one of best choices for a full range of motion control applications and it is being considered in highpower applications such as industrial drives and vehicular propulsion.
Abstract: The permanent-magnet synchronous machine (PMSM) drive is one of best choices for a full range of motion control applications. For example, the PMSM is widely used in robotics, machine tools, actuators, and it is being considered in highpower applications such as industrial drives and vehicular propulsion. It is also used for residential/commercial applications. The PMSM is known for having low torque ripple, superior dynamic performance, high efficiency and high power density. Section 1 deals with the introduction of PMSM and how it is evolved from synchronous motors. Section 2 briefly discusses about the types of PMSM. Section 3 tells about the assumptions in PMSM for modeling of PMSM and it derives the equivalent circuit of PMSM. In Section 4, permanent magnet synchronous motor drive system is briefly discussed with explanation of each blocks in the systems. Section 5 reveals about the control techniques of PMSM like scalar control, vector control and simulation of PMSM driven by field-oriented control using fuzzy logic control with space vector modulation for minimizing torque ripples. PMSM control with and without rotor position sensors along with different control techniques for controlling various parameters of PMSM for different applications is presented in Section 6.
5 citations
Dissertation•
01 Jan 2020
TL;DR: In this article, several machine learning (ML) models were evaluated on their estimation error after training on test bench data from a permanent magnet synchronous motors (PMSMs) for the task of predicting the temperatures of the rotor, yoke, stator tooth and stator winding.
Abstract: The ubiquitous adoption of permanent magnet synchronous motors (PMSMs) as the electric motors of choice for traction drive applications especially in manufacturing and the electric vehicle industries birthed the need for monitoring the temperatures of its critical components to control the effects of overheating. In proffering solutions, several techniques have been employed by researchers spanning decades. These include the sensor-based method, methods based on classic thermal theory, electric circuit theory and the hybrid lumped-parameter thermal networks (LPTNs). These however have deficiencies ranging from requiring expertise for efficient modelling to one or the other of lacking interpretability and not meeting reliability requirements. Recent studies have seen an increased application of machine learning techniques to other fields like healthcare with convincing results. In this work, several machine learning (ML) models were evaluated on their estimation error after training on test bench data from a PMSM for the task of predicting the temperatures of the rotor, stator yoke, stator tooth and stator winding. Diverse regression algorithms were applied and include linear regression (LR), k-nearest neighbours (kNN) regression, random forest (RF) and decision tree (DT). It is observed that the stator yoke records the least error of prediction while the pm records the highest and in general, the stator components record the least error compared to the rotor component.
Keywords- permanent magnet synchronous motors, machine learning, linear regression, temperature estimation, random forests
1 citations