Current harmonics suppression strategy for motor drives using machine learning algorithm

The Permanent Magnetic Synchronous Motor (PMSM) has been widely used in industrial field because of its simple structure, high accuracy, high efficiency and high power density. However, many disturbances existing in the motor control system may influence the overall performance, such as loading osci...

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Main Author: Li, Tianyi
Other Authors: Christopher H. T. Lee
Format: Thesis-Master by Coursework
Language:English
Published: Nanyang Technological University 2024
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Online Access:https://hdl.handle.net/10356/175973
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Institution: Nanyang Technological University
Language: English
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spelling sg-ntu-dr.10356-1759732024-05-10T15:50:00Z Current harmonics suppression strategy for motor drives using machine learning algorithm Li, Tianyi Christopher H. T. Lee School of Electrical and Electronic Engineering chtlee@ntu.edu.sg Engineering Permanent magnetic synchronous motor Mechanical model Current harmonics Machine learning Neural network ADALINE The Permanent Magnetic Synchronous Motor (PMSM) has been widely used in industrial field because of its simple structure, high accuracy, high efficiency and high power density. However, many disturbances existing in the motor control system may influence the overall performance, such as loading oscillations, parameters variation, friction force, temperature issue. Most of these factors may induce current harmonics, which will result in torque ripple and mechanical vibrations so that preventing the application in high performance occasions (i.e. robotics, Electrical vehicles). First of all, the existing solution to suppress harmonic current has been reviewed and divided to online compensation, offline compensation and periodical disturbance observer. The online compensation has high dynamic performance but usually need extra sensors to detect system parameters. Advanced periodical disturbance observe is effective and requires high computing ability of hardware. For offline compensation, it is hard for model-based method to adapt to different working conditions. Besides, considering the limitation of the existing strategies, the neuron network is known for its outstanding self-learning ability and excellent nonlinear approximation capacity. A neuron network-based method is proposed to suppress harmonic current. In motor control system, the dead time effect is considered as the main component to compensate. The outstanding dynamic performance of Adaptive Neuron Network (ADALINE) can perfectly cooperate with the steady performance of conventional PI controller. In the proposed method, PI controller and dual ADALINE is connected in parallel to suppress selected harmonics. Furthermore, the rationality of the proposed method is verified by the corresponding experiments. By analyzing the experimental result, the method is proved to be effective in steady, speed step, load transient, inductance transient condition, the performance of motor is optimized considerably. Also, based on the advantages drawn from the experimental result, the possible application for proposed method is briefly introduced which points out the prospect in industrial field. Master's degree 2024-05-10T07:40:01Z 2024-05-10T07:40:01Z 2024 Thesis-Master by Coursework Li, T. (2024). Current harmonics suppression strategy for motor drives using machine learning algorithm. Master's thesis, Nanyang Technological University, Singapore. https://hdl.handle.net/10356/175973 https://hdl.handle.net/10356/175973 en application/pdf Nanyang Technological University
institution Nanyang Technological University
building NTU Library
continent Asia
country Singapore
Singapore
content_provider NTU Library
collection DR-NTU
language English
topic Engineering
Permanent magnetic synchronous motor
Mechanical model
Current harmonics
Machine learning
Neural network
ADALINE
spellingShingle Engineering
Permanent magnetic synchronous motor
Mechanical model
Current harmonics
Machine learning
Neural network
ADALINE
Li, Tianyi
Current harmonics suppression strategy for motor drives using machine learning algorithm
description The Permanent Magnetic Synchronous Motor (PMSM) has been widely used in industrial field because of its simple structure, high accuracy, high efficiency and high power density. However, many disturbances existing in the motor control system may influence the overall performance, such as loading oscillations, parameters variation, friction force, temperature issue. Most of these factors may induce current harmonics, which will result in torque ripple and mechanical vibrations so that preventing the application in high performance occasions (i.e. robotics, Electrical vehicles). First of all, the existing solution to suppress harmonic current has been reviewed and divided to online compensation, offline compensation and periodical disturbance observer. The online compensation has high dynamic performance but usually need extra sensors to detect system parameters. Advanced periodical disturbance observe is effective and requires high computing ability of hardware. For offline compensation, it is hard for model-based method to adapt to different working conditions. Besides, considering the limitation of the existing strategies, the neuron network is known for its outstanding self-learning ability and excellent nonlinear approximation capacity. A neuron network-based method is proposed to suppress harmonic current. In motor control system, the dead time effect is considered as the main component to compensate. The outstanding dynamic performance of Adaptive Neuron Network (ADALINE) can perfectly cooperate with the steady performance of conventional PI controller. In the proposed method, PI controller and dual ADALINE is connected in parallel to suppress selected harmonics. Furthermore, the rationality of the proposed method is verified by the corresponding experiments. By analyzing the experimental result, the method is proved to be effective in steady, speed step, load transient, inductance transient condition, the performance of motor is optimized considerably. Also, based on the advantages drawn from the experimental result, the possible application for proposed method is briefly introduced which points out the prospect in industrial field.
author2 Christopher H. T. Lee
author_facet Christopher H. T. Lee
Li, Tianyi
format Thesis-Master by Coursework
author Li, Tianyi
author_sort Li, Tianyi
title Current harmonics suppression strategy for motor drives using machine learning algorithm
title_short Current harmonics suppression strategy for motor drives using machine learning algorithm
title_full Current harmonics suppression strategy for motor drives using machine learning algorithm
title_fullStr Current harmonics suppression strategy for motor drives using machine learning algorithm
title_full_unstemmed Current harmonics suppression strategy for motor drives using machine learning algorithm
title_sort current harmonics suppression strategy for motor drives using machine learning algorithm
publisher Nanyang Technological University
publishDate 2024
url https://hdl.handle.net/10356/175973
_version_ 1800916401642274816