Online neural network application for compensation of the VSI voltage nonlinearities

التفاصيل البيبلوغرافية
العنوان: Online neural network application for compensation of the VSI voltage nonlinearities
المؤلفون: Buchta, Luděk, Kozovský, Matúš
بيانات النشر: IEEE
سنة النشر: 2023
المجموعة: Brno University of Technology (VUT): Digital Library / Vysoké učení technické v Brně: Digitální knihovně
مصطلحات موضوعية: dead-time compensation, artificial neural network (ANN), voltage source inverter (VSI), permanent magnet synchronous motor (PMSM)
الوصف: The paper aims to solve the distortion problem of the inverter output voltages that cause harmonic deformation of the phase currents and ripple of dq- currents of the three-phase permanent magnet synchronous motor (PMSM). The inverter non-linearities adversely affect the effectiveness of the PMSM control algorithm. The compensation strategy is based on the neural network and knowledge of the three-phase PMSM model structure and its parameters. The input data for the neural network consist of the normed values and detected polarities of the phase currents and rotor position information. As a result, the proposed artificial neural network (ANN) can extract non-linear functions from the measured data to compensate for the VSI output voltages. The ANN is designed to learn online while the PMSM is running. The back-propagation algorithm is used for neural network learning. The proposed stratégy was implemented in an AURIX TC397 microcontroller and validated by experiments on a real PMSM. The presented results demonstrate the effectiveness of the proposed solution.
نوع الوثيقة: conference object
وصف الملف: text; 1-6; application/pdf
اللغة: English
ردمك: 979-83-503-3182-0
Relation: IECON 2023- 49th Annual Conference of the IEEE Industrial Electronics Society; https://ieeexplore.ieee.org/document/10312305; IECON 2023- 49th Annual Conference of the IEEE Industrial Electronics Society. 2023, p. 1-6.; 979-8-3503-3182-0; 185462; http://hdl.handle.net/11012/245227; orcid:0000-0002-8954-3495; orcid:0000-0002-1547-1003; G-8085-2014; E-2371-2018; 56028720700
DOI: 10.1109/IECON51785.2023.10312305
الاتاحة: http://hdl.handle.net/11012/245227
https://doi.org/10.1109/IECON51785.2023.10312305
Rights: (C) IEEE ; openAccess
رقم الانضمام: edsbas.17374F26
قاعدة البيانات: BASE
الوصف
ردمك:9798350331820
DOI:10.1109/IECON51785.2023.10312305