Current Harmonics Minimization of Permanent Magnet Synchronous Machine Based on Iterative Learning Control and Neural Networks
Electrical machines generate unwanted flux and current harmonics. Harmonics can be suppressed using various methods. In this paper, the harmonics are significantly reduced using Iterative Learning Control (ILC) and Neural Networks (NNs). The ILC can compensate for the harmonics well for operation at...
| Main Authors: | , , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
MDPI AG
2023-07-01
|
| Series: | Machines |
| Subjects: | |
| Online Access: | https://www.mdpi.com/2075-1702/11/8/784 |
| _version_ | 1797584010176626688 |
|---|---|
| author | Annette Mai Xinjun Liu Bernhard Wagner Maximilian Hofmann |
| author_facet | Annette Mai Xinjun Liu Bernhard Wagner Maximilian Hofmann |
| author_sort | Annette Mai |
| collection | DOAJ |
| description | Electrical machines generate unwanted flux and current harmonics. Harmonics can be suppressed using various methods. In this paper, the harmonics are significantly reduced using Iterative Learning Control (ILC) and Neural Networks (NNs). The ILC can compensate for the harmonics well for operation at constant speed and current reference values. The NNs are trained with the data from the ILC and help to suppress the harmonics well even in transient operation. The simulation model is based on flux and torque maps, depending on <inline-formula><math xmlns="http://www.w3.org/1998/Math/MathML" display="inline"><semantics><mrow><mi>d</mi><mi>q</mi></mrow></semantics></math></inline-formula>-currents and the electrical angle. The maps are generated from FEM simulation of an interior permanent magnet synchronous machine (IPM) and are published with the paper. They are intended to serve other researchers for direct comparison with their own methods. Simulation results in this paper verify that by using ILC and NNs together, current harmonics in transient operation can be eliminated better than without NNs. |
| first_indexed | 2024-03-10T23:47:10Z |
| format | Article |
| id | doaj.art-13fd11991b164534bc8e92b8871dfc05 |
| institution | Directory Open Access Journal |
| issn | 2075-1702 |
| language | English |
| last_indexed | 2024-03-10T23:47:10Z |
| publishDate | 2023-07-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Machines |
| spelling | doaj.art-13fd11991b164534bc8e92b8871dfc052023-11-19T01:56:36ZengMDPI AGMachines2075-17022023-07-0111878410.3390/machines11080784Current Harmonics Minimization of Permanent Magnet Synchronous Machine Based on Iterative Learning Control and Neural NetworksAnnette Mai0Xinjun Liu1Bernhard Wagner2Maximilian Hofmann3Fraunhofer Institute IISB, 91058 Erlangen, GermanyFraunhofer Institute IISB, 91058 Erlangen, GermanyTechnische Hochschule Nürnberg Georg Simon Ohm, 90489 Nürnberg, GermanyFraunhofer Institute IISB, 91058 Erlangen, GermanyElectrical machines generate unwanted flux and current harmonics. Harmonics can be suppressed using various methods. In this paper, the harmonics are significantly reduced using Iterative Learning Control (ILC) and Neural Networks (NNs). The ILC can compensate for the harmonics well for operation at constant speed and current reference values. The NNs are trained with the data from the ILC and help to suppress the harmonics well even in transient operation. The simulation model is based on flux and torque maps, depending on <inline-formula><math xmlns="http://www.w3.org/1998/Math/MathML" display="inline"><semantics><mrow><mi>d</mi><mi>q</mi></mrow></semantics></math></inline-formula>-currents and the electrical angle. The maps are generated from FEM simulation of an interior permanent magnet synchronous machine (IPM) and are published with the paper. They are intended to serve other researchers for direct comparison with their own methods. Simulation results in this paper verify that by using ILC and NNs together, current harmonics in transient operation can be eliminated better than without NNs.https://www.mdpi.com/2075-1702/11/8/784PMSMPSMiterative learning controlrepetitive controlharmonicsneural networks |
| spellingShingle | Annette Mai Xinjun Liu Bernhard Wagner Maximilian Hofmann Current Harmonics Minimization of Permanent Magnet Synchronous Machine Based on Iterative Learning Control and Neural Networks Machines PMSM PSM iterative learning control repetitive control harmonics neural networks |
| title | Current Harmonics Minimization of Permanent Magnet Synchronous Machine Based on Iterative Learning Control and Neural Networks |
| title_full | Current Harmonics Minimization of Permanent Magnet Synchronous Machine Based on Iterative Learning Control and Neural Networks |
| title_fullStr | Current Harmonics Minimization of Permanent Magnet Synchronous Machine Based on Iterative Learning Control and Neural Networks |
| title_full_unstemmed | Current Harmonics Minimization of Permanent Magnet Synchronous Machine Based on Iterative Learning Control and Neural Networks |
| title_short | Current Harmonics Minimization of Permanent Magnet Synchronous Machine Based on Iterative Learning Control and Neural Networks |
| title_sort | current harmonics minimization of permanent magnet synchronous machine based on iterative learning control and neural networks |
| topic | PMSM PSM iterative learning control repetitive control harmonics neural networks |
| url | https://www.mdpi.com/2075-1702/11/8/784 |
| work_keys_str_mv | AT annettemai currentharmonicsminimizationofpermanentmagnetsynchronousmachinebasedoniterativelearningcontrolandneuralnetworks AT xinjunliu currentharmonicsminimizationofpermanentmagnetsynchronousmachinebasedoniterativelearningcontrolandneuralnetworks AT bernhardwagner currentharmonicsminimizationofpermanentmagnetsynchronousmachinebasedoniterativelearningcontrolandneuralnetworks AT maximilianhofmann currentharmonicsminimizationofpermanentmagnetsynchronousmachinebasedoniterativelearningcontrolandneuralnetworks |