scholarly journals PERMANENT SYNCHRON MAGNET MOTOR SPEED OBSERVER BASED ON LEAST SQUARES SUPPORT VECTOR MACHINE REGRESSION

2020 ◽  
Vol 13 (2) ◽  
pp. 17-24
Author(s):  
Muldi Yuhendri ◽  
Hambali Hambali ◽  
Mukhlidi Muskhir
Keyword(s):  
Support Vector Machine ◽  
Least Squares ◽  
Permanent Magnet ◽  
Permanent Magnet Synchronous Motor ◽  
Synchronous Motor ◽  
Support Vector ◽  
Motor Speed ◽  
Support Vector Machine Regression ◽  
Adaptation Mechanism ◽  
Speed Sensor

Motor speed control requires motor speed data as feedback from control actions. Motor speed data is usually obtained from the speed sensor. In this paper, the motor speed observer for permanent magnet synchronous motor is proposed to obtain motor speed data based on motor back emf voltage making it more economical without a speed sensor. The Speed observer is designed based on the Model Reference Adapative System (MRAS) with using Least Squares Support Vector Machine Regression (LSSVMR) algorithm for adaptation mechanism tools. The proposed speed observer is tested with varying motor speeds. The test results show that the MRAS-based motor speed observer using LSSVMR has successfully estimated the rotation speed of the permanent magnet synchronous motor based on the back emf motor voltage. It can be seen from the maximum error of  the motor speed, ie only 3.7 rpm at transient conditions and close to zero at steady state

scholarly journals Support-Vector Machine Approach for Robust Fault Diagnosis of Electric Vehicle Permanent Magnet Synchronous Motor

2020 ◽  
Vol 12 (1) ◽  
pp. 10
Author(s):  
Chunheng Zhao ◽  
Yi Li ◽  
Matthew Wessner ◽  
Chinmay Rathod ◽  
Pierluigi Pisu
Keyword(s):  
Support Vector Machine ◽  
Fault Diagnosis ◽  
Permanent Magnet ◽  
High Performance ◽  
Permanent Magnet Synchronous Motor ◽  
Synchronous Motor ◽  
Industrial Applications ◽  
Operating Conditions ◽  
Support Vector ◽  
Svm Classifier

Permanent magnet synchronous motor (PMSM) is a leading technology for electric vehicles (EVs) and other high-performance industrial applications. These challenging applications demand robust fault diagnosis schemes, but conventional strategies based on models, system knowledge, and signal transformation have limitations that degrade the agility of diagnosing faults. These methods require extremely detailed design and consideration to remain robust against noise and disturbances in the actual application. Recent advancements in artificial intelligence and machine learning have proven to be promising next-generation solutions for fault diagnosis. In this paper, a support-vector machine (SVM) utilizing sparse representation is developed to perform sensor fault diagnosis of a PMSM. A simulation model of the pertinent PMSM drive system for automotive applications is used to generate a set of labelled training example sets that the SVM uses to determine margins between normal and faulty operating conditions. The PMSM model includes input as a torque reference profile and disturbance as a constant road grade, against both of which faults must be detectable. Even with limited training, the SVM classifier developed in this paper is capable of diagnosing faults with a high degree of accuracy, suggesting that such methods are feasible for the demanding fault diagnosis challenge in PMSM.

scholarly journals Research on an Improved Method for Permanent Magnet Synchronous Motor

2014 ◽  
Vol 2014 ◽  
pp. 1-7
Author(s):  
Yingpei Liu ◽  
Tao Gao ◽  
Guo Li
Keyword(s):  
Vector Control ◽  
Permanent Magnet ◽  
Permanent Magnet Synchronous Motor ◽  
Synchronous Motor ◽  
Support Vector ◽  
Improved Method ◽  
Speed Sensor ◽  
Disturbance Rejection Control ◽  
Speed Regulator ◽  
Vector Control System

In permanent magnet synchronous motor (PMSM) traditional vector control system, PI regulator is used in the speed loop, but it has some defects. An improved method of PMSM vector control is proposed in the paper. The active-disturbance rejection control (ADRC) speed regulator is designed with the input signals of given speed and real speed and the output of given stator currentqcoordinate component. Then, in order to optimize ADRC controller, the least squares support vector machines (LSSVM) optimal regression model is derived and successfully embedded in the ADRC controller. ADRC observation precision and dynamic response of the system are improved. The load disturbance effect on the system is reduced to a large extent. The system anti-interference ability is further improved. Finally, the current sensor CSNE151-100 is selected to sample PMSM stator currents. The voltage sensor JLBV1 is used to sample the stator voltage. The rotor speed of PMSM is measured by mechanical speed sensor, the type of which is BENTLY 330500. Experimental platform is constructed to verify the effectiveness of the proposed method.

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