scholarly journals Chaos modeling and real-time online prediction of permanent magnet synchronous motor based on multiple kernel least squares support vector machine

2010 ◽  
Vol 59 (4) ◽  
pp. 2310
Author(s):  
Chen Qiang ◽  
Ren Xue-Mei
Keyword(s):  
Support Vector Machine ◽  
Least Squares ◽  
Permanent Magnet ◽  
Real Time ◽  
Permanent Magnet Synchronous Motor ◽  
Synchronous Motor ◽  
Support Vector ◽  
Multiple Kernel ◽  
Online Prediction

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.

A computationally efficient torque and flux ripple reduction by active vector optimization approach using direct torque control–based model predictive torque control for matrix converter–fed permanent magnet synchronous motor

2022 ◽  
pp. 014233122110688
Author(s):  
JD Anunciya ◽  
Arumugam Sivaprakasam
Keyword(s):  
Permanent Magnet ◽  
Real Time ◽  
Permanent Magnet Synchronous Motor ◽  
Direct Torque Control ◽  
Synchronous Motor ◽  
Matrix Converter ◽  
Torque Control ◽  
Optimization Approach ◽  
The Real ◽  
The Matrix

The Matrix Converter–fed Finite Control Set–Model Predictive Control is an efficient drive control approach that exhibits numerous advantageous features. However, it is computationally expensive as it employs all the available matrix converter voltage vectors for the prediction and estimation. The computational complexity increases further with respect to the inclusion of additional control objectives in the cost function which degrades the potentiality of this technique. This paper proposes two computationally effective switching tables for simplifying the calculation process and optimizing the matrix converter active prediction vectors. Here, three prediction active vectors are selected out of 18 vectors by considering the torque and flux errors of the permanent magnet synchronous motor. In addition, the voltage vector location segments are modified into 12 sectors to boost the torque dynamic control. The performance superiority of the proposed concept is analyzed using the MATLAB/Simulink software and the real-time validation is conducted by implementing in the real-time OPAL-RT lab setup.

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