2019 IEEE 12th International Symposium on Diagnostics for Electrical Machines, Power Electronics and Drives (SDEMPED)最新文献_第3页

Optimizing the Generator Critical Clearing Time using Super Capacitor Energy Storage in the Grid Power System with Differential Evolution Algorithm 基于差分进化算法的超级电容储能在电网系统中优化发电机临界清净时间

2019 IEEE 12th International Symposium on Diagnostics for Electrical Machines, Power Electronics and Drives (SDEMPED) Pub Date : 2019-08-01 DOI: 10.1109/DEMPED.2019.8864843

P. Talitha, I. Hafiz, R. Vincentius, P. Ardyono, L. Vita, H. Mauridhi

引用次数: 0

A Study of Static Angular and Axis Eccentricity in a Double-Sided Rotor AFPM Generator using 3D-FEM 双面转子AFPM发电机静态角偏和轴偏的三维有限元研究

2019 IEEE 12th International Symposium on Diagnostics for Electrical Machines, Power Electronics and Drives (SDEMPED) Pub Date : 2019-08-01 DOI: 10.1109/DEMPED.2019.8864807

A. C. Barmpatza, J. Kappatou

引用次数: 4

Estimating the Impact of Pulse Voltage Stress Caused by Modern Power Electronics Technology on Machine Winding Insulation Material 现代电力电子技术引起的脉冲电压应力对机器绕组绝缘材料影响的估计

2019 IEEE 12th International Symposium on Diagnostics for Electrical Machines, Power Electronics and Drives (SDEMPED) Pub Date : 2019-08-01 DOI: 10.1109/DEMPED.2019.8864833

A. Qerkini, M. Vogelsberger, P. Macheiner, W. Grubelnik, H. Ertl, T. Wolbank

{"title":"Estimating the Impact of Pulse Voltage Stress Caused by Modern Power Electronics Technology on Machine Winding Insulation Material","authors":"A. Qerkini, M. Vogelsberger, P. Macheiner, W. Grubelnik, H. Ertl, T. Wolbank","doi":"10.1109/DEMPED.2019.8864833","DOIUrl":"https://doi.org/10.1109/DEMPED.2019.8864833","url":null,"abstract":"Modern electric drives are generally driven by voltage source inverters. With the aim of reducing size and losses, switching speed of modern power electronic devices is steadily increasing. At the same time, it can be observed that operation under repetitive fast voltage transitions is reducing reliability and lifetime of motor insulation due to the additional stress. Available data for allowable voltage pulse stress in insulation material is usually limited to voltage pulses having rise time below 300ns and dv/dt below several kV/µs. With the introduction of wide bandgap power electronic devices, dv/dt will be significantly increased leaving the question about the impact on resulting insulation life time. In this paper insulation life time tests are presented under operating conditions similar to medium voltage SiC technology. Reversible and irreversible effects within the material are identified and their connection to switching speed and pulse voltage magnitude shown.","PeriodicalId":397001,"journal":{"name":"2019 IEEE 12th International Symposium on Diagnostics for Electrical Machines, Power Electronics and Drives (SDEMPED)","volume":"214 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115750480","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 2

Stator fault diagnosis by reactive power in dual three-phase reluctance motors 双三相磁阻电动机定子故障无功诊断

2019 IEEE 12th International Symposium on Diagnostics for Electrical Machines, Power Electronics and Drives (SDEMPED) Pub Date : 2019-08-01 DOI: 10.1109/DEMPED.2019.8864909

C. Bianchini, A. Torreggiani, M. Davoli, A. Bellini, Cristian Babetto, N. Bianchi

引用次数: 3

Towards Advanced Diagnosis Recognition for Eccentricities Faults: Application on Induction Motor 偏心故障的高级诊断识别:在感应电动机上的应用

2019 IEEE 12th International Symposium on Diagnostics for Electrical Machines, Power Electronics and Drives (SDEMPED) Pub Date : 2019-08-01 DOI: 10.1109/DEMPED.2019.8864920

I. Bouchareb, A. Lebaroud, A. Cardoso, S. Lee

{"title":"Towards Advanced Diagnosis Recognition for Eccentricities Faults: Application on Induction Motor","authors":"I. Bouchareb, A. Lebaroud, A. Cardoso, S. Lee","doi":"10.1109/DEMPED.2019.8864920","DOIUrl":"https://doi.org/10.1109/DEMPED.2019.8864920","url":null,"abstract":"Artificial Intelligence (AI) is expected to be a large driver in industrial applications competitiveness in the not-so-distant future. Induction motors (IMs) are used worldwide as the “workhorse” in industrial applications. The paper reviews the possibility of integrating artificial intelligence techniques for condition monitoring and fault diagnosis of induction motors so-called advanced diagnosis. The paper focuses on advanced diagnosis method related on the recognition, classification and prognostics of eccentricities faults in induction motor drives. Rotor eccentricity has been the aim of many researchers. However reliably detection and accurate prediction of eccentricity fault is still not possible and difficult task if appear individually. To face this situation, an intelligent diagnosis system merges Neural Network and Hidden Markov Model together (NN-HMM) into a common framework to overcome the deficiencies of eccentricity diagnosis. Current measurements based on non-parametrical Time-Frequency Representation (TFR) are used for features extraction. Then, a features selection method using Fisher's Discriminant Ratio (FDR) is applied to select an optimal number of the extracted features associated with polynomial approach to track, recognize of various eccentricities faults types and degree precisely. An experimental study on a 7.5h induction motor prove the reliability and the efficiency of the proposed method in condition monitoring of eccentricities with different degree 0%, 20%, 40%, 60, 80% precisely independent of load or motor type.","PeriodicalId":397001,"journal":{"name":"2019 IEEE 12th International Symposium on Diagnostics for Electrical Machines, Power Electronics and Drives (SDEMPED)","volume":"115 11","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131549493","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 3

Fabrication and Dielectric Breakdown of 3C-SiC/SiO2 MOS Capacitors 3C-SiC/SiO2 MOS电容器的制备及其介电击穿

2019 IEEE 12th International Symposium on Diagnostics for Electrical Machines, Power Electronics and Drives (SDEMPED) Pub Date : 2019-08-01 DOI: 10.1109/DEMPED.2019.8864836

Fan Li, Song Qiu, M. Jennings, P. Mawby

引用次数: 2

On the robustness of ultra-high voltage 4H-SiC IGBTs with an optimized retrograde p-well 超高压4H-SiC igbt的稳健性研究

2019 IEEE 12th International Symposium on Diagnostics for Electrical Machines, Power Electronics and Drives (SDEMPED) Pub Date : 2019-08-01 DOI: 10.1109/DEMPED.2019.8864804

Amit K. Tiwari, S. Perkin, N. Lophitis, Marina Antoniou, T. Trajkovic, F. Udrea

引用次数: 4

On-line Transmission Line Fault Classification using Long Short-Term Memory 基于长短期记忆的在线输电线路故障分类

2019 IEEE 12th International Symposium on Diagnostics for Electrical Machines, Power Electronics and Drives (SDEMPED) Pub Date : 2019-08-01 DOI: 10.1109/DEMPED.2019.8864831

Mengshi Li, Yaozhou Yu, T. Ji, Qinghua Wu

{"title":"On-line Transmission Line Fault Classification using Long Short-Term Memory","authors":"Mengshi Li, Yaozhou Yu, T. Ji, Qinghua Wu","doi":"10.1109/DEMPED.2019.8864831","DOIUrl":"https://doi.org/10.1109/DEMPED.2019.8864831","url":null,"abstract":"In order to perform on-line transmission line fault diagnosis, this paper proposes a classification algorithm, which combines the long short-term memory (LSTM) network with a calibration training filter. The LSTM network adopted in this research is a multilayer recurrent neural network. As a deep learning algorithm, LSTM is extremely suitable to complex time-series classification problems, such as speech recognition and natural language processing. As the number of units in LSTM is much larger than conventional artificial neural networks (ANNs), the training progress is time consuming, and not able to be performed by on-line diagnosis devices. However, the parameters of the transmission line are always varying with time, which requires frequently calibration training on the network. In order to accelerate the calibration training of LSTM, a filter enhanced calibration is proposed. The filter selects samples having the same pattern as the signal under diagnosis, and further reduces the training complexity. The experimental study compares the proposed filter calibrated LSTM (FC-LSTM) against other neural networks and machine learning algorithms on a on-line test model. The numerical comparison not only shows FC-LSTM has a better classification accuracy and a very short time delay.","PeriodicalId":397001,"journal":{"name":"2019 IEEE 12th International Symposium on Diagnostics for Electrical Machines, Power Electronics and Drives (SDEMPED)","volume":"629 ","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"113994335","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 4

A Thermographic Method to Evaluate Different Processes Effects on Magnetic Steels 用热像法评价不同工艺对磁性钢的影响

2019 IEEE 12th International Symposium on Diagnostics for Electrical Machines, Power Electronics and Drives (SDEMPED) Pub Date : 2019-08-01 DOI: 10.1109/DEMPED.2019.8864852

E. Pošković, L. Ferraris, G. Bramerdorfer, M. Cossale

引用次数: 0

Overview of Condition Monitoring Systems for Power Distribution Grids 配电网状态监测系统综述

2019 IEEE 12th International Symposium on Diagnostics for Electrical Machines, Power Electronics and Drives (SDEMPED) Pub Date : 2019-08-01 DOI: 10.1109/DEMPED.2019.8864872

D. Martinez, H. Henao, G. Capolino

引用次数: 4