{"title":"Ultra-Sensitive Monitoring of Line-End Coil Insulation Degradation in Inverter-Fed Machine Using PT Symmetry","authors":"Hao Li;Helong Yang;Dawei Xiang;Jinyu Chen","doi":"10.1109/TIE.2024.3525108","DOIUrl":null,"url":null,"abstract":"The line-end coil insulation, a vulnerable point in winding insulation, is subjected to higher transient voltage stress and a greater risk of breakdown, making it both attractive and challenging to monitor at an early stage. To improve the sensitivity of early line-end coil insulation monitoring, this article proposes a novel approach based on parity-time (PT) symmetry, utilizing an external resonator magnetically coupled with the system's high-frequency common-mode (HFCM) switching oscillation. The effect of damping rate splitting near the exceptional point (EP) is used to detect early degradation of line-end coil insulation with enhanced sensitivity. Initially, the HFCM switching oscillation in the inverter-fed machine system is considered an internal resonator, and its modal features are analyzed. Subsequently, an external resonator is designed to operate at the EP of PT symmetry, and the extracted damping rate feature of the HFCM switching current is explored for online monitoring. Experimental results demonstrate that insulation capacitance degradation can be effectively identified with a sensitivity of approximately 0.8%, which is at least six times greater than that achieved by the conventional method utilizing resonant frequency characteristics. This method is characterized by its noncontact safety, ultra-sensitivity, and robustness.","PeriodicalId":13402,"journal":{"name":"IEEE Transactions on Industrial Electronics","volume":"72 8","pages":"8612-8622"},"PeriodicalIF":7.2000,"publicationDate":"2025-01-16","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/10843746/","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}
引用次数: 0
Abstract
The line-end coil insulation, a vulnerable point in winding insulation, is subjected to higher transient voltage stress and a greater risk of breakdown, making it both attractive and challenging to monitor at an early stage. To improve the sensitivity of early line-end coil insulation monitoring, this article proposes a novel approach based on parity-time (PT) symmetry, utilizing an external resonator magnetically coupled with the system's high-frequency common-mode (HFCM) switching oscillation. The effect of damping rate splitting near the exceptional point (EP) is used to detect early degradation of line-end coil insulation with enhanced sensitivity. Initially, the HFCM switching oscillation in the inverter-fed machine system is considered an internal resonator, and its modal features are analyzed. Subsequently, an external resonator is designed to operate at the EP of PT symmetry, and the extracted damping rate feature of the HFCM switching current is explored for online monitoring. Experimental results demonstrate that insulation capacitance degradation can be effectively identified with a sensitivity of approximately 0.8%, which is at least six times greater than that achieved by the conventional method utilizing resonant frequency characteristics. This method is characterized by its noncontact safety, ultra-sensitivity, and robustness.
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.