{"title":"无传感器控制下基于虚拟反向电磁场注入的表面贴装式 PMSM 电机在线参数识别","authors":"Peng Wang;Z. Q. Zhu;Dawei Liang","doi":"10.1109/TIE.2024.3488273","DOIUrl":null,"url":null,"abstract":"Rotor position error under sensorless control will lead to parameter identification errors of surface-mounted permanent magnet (PM) synchronous machines (SPMSMs). In this article, a novel signal injection method is proposed for the first time, i.e., two additional virtual back-electromotive forces (EMFs) with equal amplitudes as perturbation signals are injected into a position observer. By injecting a positive and a negative virtual back-EMF, it is found that position error caused by parameter errors and inverter nonlinearity maintains constant and is only related to the ratio of <italic>q</i>- and <italic>d</i>-axis voltage fluctuations. As a result, the correlations of phase inductance, resistance, and PM flux linkage with the measured <italic>dq</i>-axis currents/voltages can be established, in which the influence of both position error and inverter nonlinearity can be completely cancelled. Subsequently, by developing a full-rank model under two <italic>d</i>-axis current conditions, the phase resistance, inductance, and PM flux linkage can be accurately identified. The experiment results show good performance of the proposed identification method under sensorless control.","PeriodicalId":13402,"journal":{"name":"IEEE Transactions on Industrial Electronics","volume":"72 6","pages":"5579-5590"},"PeriodicalIF":7.2000,"publicationDate":"2024-11-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Virtual Back-EMF Injection Based Online Parameter Identification of Surface-Mounted PMSMs Under Sensorless Control\",\"authors\":\"Peng Wang;Z. Q. Zhu;Dawei Liang\",\"doi\":\"10.1109/TIE.2024.3488273\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"Rotor position error under sensorless control will lead to parameter identification errors of surface-mounted permanent magnet (PM) synchronous machines (SPMSMs). In this article, a novel signal injection method is proposed for the first time, i.e., two additional virtual back-electromotive forces (EMFs) with equal amplitudes as perturbation signals are injected into a position observer. By injecting a positive and a negative virtual back-EMF, it is found that position error caused by parameter errors and inverter nonlinearity maintains constant and is only related to the ratio of <italic>q</i>- and <italic>d</i>-axis voltage fluctuations. As a result, the correlations of phase inductance, resistance, and PM flux linkage with the measured <italic>dq</i>-axis currents/voltages can be established, in which the influence of both position error and inverter nonlinearity can be completely cancelled. Subsequently, by developing a full-rank model under two <italic>d</i>-axis current conditions, the phase resistance, inductance, and PM flux linkage can be accurately identified. The experiment results show good performance of the proposed identification method under sensorless control.\",\"PeriodicalId\":13402,\"journal\":{\"name\":\"IEEE Transactions on Industrial Electronics\",\"volume\":\"72 6\",\"pages\":\"5579-5590\"},\"PeriodicalIF\":7.2000,\"publicationDate\":\"2024-11-12\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"IEEE Transactions on Industrial Electronics\",\"FirstCategoryId\":\"94\",\"ListUrlMain\":\"https://ieeexplore.ieee.org/document/10750528/\",\"RegionNum\":1,\"RegionCategory\":\"工程技术\",\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q1\",\"JCRName\":\"AUTOMATION & CONTROL SYSTEMS\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"IEEE Transactions on Industrial Electronics","FirstCategoryId":"94","ListUrlMain":"https://ieeexplore.ieee.org/document/10750528/","RegionNum":1,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q1","JCRName":"AUTOMATION & CONTROL SYSTEMS","Score":null,"Total":0}
Virtual Back-EMF Injection Based Online Parameter Identification of Surface-Mounted PMSMs Under Sensorless Control
Rotor position error under sensorless control will lead to parameter identification errors of surface-mounted permanent magnet (PM) synchronous machines (SPMSMs). In this article, a novel signal injection method is proposed for the first time, i.e., two additional virtual back-electromotive forces (EMFs) with equal amplitudes as perturbation signals are injected into a position observer. By injecting a positive and a negative virtual back-EMF, it is found that position error caused by parameter errors and inverter nonlinearity maintains constant and is only related to the ratio of q- and d-axis voltage fluctuations. As a result, the correlations of phase inductance, resistance, and PM flux linkage with the measured dq-axis currents/voltages can be established, in which the influence of both position error and inverter nonlinearity can be completely cancelled. Subsequently, by developing a full-rank model under two d-axis current conditions, the phase resistance, inductance, and PM flux linkage can be accurately identified. The experiment results show good performance of the proposed identification method under sensorless control.
期刊介绍:
Journal Name: IEEE Transactions on Industrial Electronics
Publication Frequency: Monthly
Scope:
The scope of IEEE Transactions on Industrial Electronics encompasses the following areas:
Applications of electronics, controls, and communications in industrial and manufacturing systems and processes.
Power electronics and drive control techniques.
System control and signal processing.
Fault detection and diagnosis.
Power systems.
Instrumentation, measurement, and testing.
Modeling and simulation.
Motion control.
Robotics.
Sensors and actuators.
Implementation of neural networks, fuzzy logic, and artificial intelligence in industrial systems.
Factory automation.
Communication and computer networks.