Pub Date : 2019-08-01DOI: 10.1109/DEMPED.2019.8864843
P. Talitha, I. Hafiz, R. Vincentius, P. Ardyono, L. Vita, H. Mauridhi
Electrical machines such as generator can lose its synchronization due to the oscillation when a large disturbance happens. It becomes the primary concern in power stability, especially in transient stability because it leads to blackout condition. This paper proposed the addition of Super Capacitor Energy Storage (SCES) by absorbing the excess power when a disturbance happens. Equal Area Criterion (EAC) is used to obtain the value of Critical Clearing Time (CCT). The simulation is conducted in Single Machine Infinite Bus (SMIB). The value of CCT before adding SCES is 0.272s, while after adding SCES it becomes 0.485s. In order to optimize the CCT, a Differential Evolution (DE) Algorithm is used. In this paper, SCES strengthening components (KSCES) used as the optimized parameter. As a result, the value of SCES becomes 0.574s, which is higher than before adding SCES and before optimizing the parameter of SCES.
{"title":"Optimizing the Generator Critical Clearing Time using Super Capacitor Energy Storage in the Grid Power System with Differential Evolution Algorithm","authors":"P. Talitha, I. Hafiz, R. Vincentius, P. Ardyono, L. Vita, H. Mauridhi","doi":"10.1109/DEMPED.2019.8864843","DOIUrl":"https://doi.org/10.1109/DEMPED.2019.8864843","url":null,"abstract":"Electrical machines such as generator can lose its synchronization due to the oscillation when a large disturbance happens. It becomes the primary concern in power stability, especially in transient stability because it leads to blackout condition. This paper proposed the addition of Super Capacitor Energy Storage (SCES) by absorbing the excess power when a disturbance happens. Equal Area Criterion (EAC) is used to obtain the value of Critical Clearing Time (CCT). The simulation is conducted in Single Machine Infinite Bus (SMIB). The value of CCT before adding SCES is 0.272s, while after adding SCES it becomes 0.485s. In order to optimize the CCT, a Differential Evolution (DE) Algorithm is used. In this paper, SCES strengthening components (KSCES) used as the optimized parameter. As a result, the value of SCES becomes 0.574s, which is higher than before adding SCES and before optimizing the parameter of SCES.","PeriodicalId":397001,"journal":{"name":"2019 IEEE 12th International Symposium on Diagnostics for Electrical Machines, Power Electronics and Drives (SDEMPED)","volume":"98 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":"132981369","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}
Pub Date : 2019-08-01DOI: 10.1109/DEMPED.2019.8864807
A. C. Barmpatza, J. Kappatou
This paper investigates the static angular and the static axis eccentricity faults in an Axial Flux Permanent Magnet (AFPM) Synchronous Generator using 3D-FEM. The machine has been constructed in the laboratory and it has a double-sided rotor and a coreless stator. The phase EMF waveforms, the corresponding spectra and the spectra of the stator current are investigated for the faulty cases. In addition, the spectrum of the phase EMF sum (Vs) is studied for fault diagnosis purposes. The novelty of the paper is that the Vs spectrum has not been used previously for fault identification in AFPM machines, as well as the stator current spectrum has not been analyzed before for a double-sided rotor, coreless stator topology with static axis eccentricity fault.
{"title":"A Study of Static Angular and Axis Eccentricity in a Double-Sided Rotor AFPM Generator using 3D-FEM","authors":"A. C. Barmpatza, J. Kappatou","doi":"10.1109/DEMPED.2019.8864807","DOIUrl":"https://doi.org/10.1109/DEMPED.2019.8864807","url":null,"abstract":"This paper investigates the static angular and the static axis eccentricity faults in an Axial Flux Permanent Magnet (AFPM) Synchronous Generator using 3D-FEM. The machine has been constructed in the laboratory and it has a double-sided rotor and a coreless stator. The phase EMF waveforms, the corresponding spectra and the spectra of the stator current are investigated for the faulty cases. In addition, the spectrum of the phase EMF sum (Vs) is studied for fault diagnosis purposes. The novelty of the paper is that the Vs spectrum has not been used previously for fault identification in AFPM machines, as well as the stator current spectrum has not been analyzed before for a double-sided rotor, coreless stator topology with static axis eccentricity fault.","PeriodicalId":397001,"journal":{"name":"2019 IEEE 12th International Symposium on Diagnostics for Electrical Machines, Power Electronics and Drives (SDEMPED)","volume":"127 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":"115616400","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}
Pub Date : 2019-08-01DOI: 10.1109/DEMPED.2019.8864833
A. Qerkini, M. Vogelsberger, P. Macheiner, W. Grubelnik, H. Ertl, T. Wolbank
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.
{"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}
Pub Date : 2019-08-01DOI: 10.1109/DEMPED.2019.8864909
C. Bianchini, A. Torreggiani, M. Davoli, A. Bellini, Cristian Babetto, N. Bianchi
Electrical machines are wide spread because of their intrinsic robustness, versatility and reduced impact on energy and resources. Recently, the demand has focused on specific features: compatibility with power converters and fault tolerance. In fact, a wide range of applications require variable speed drives and high rejection of faults, i.e. safe operation also for non-critical applications. Here, a dual three-phase stator configuration is used, and a novel method for stator fault detection is presented. This method is based on reactive power measurements from both three-phase systems. A differential diagnostic index is defined, that can be used to detect effectively stator faults, isolating them from torque oscillations, load unbalances or other pitfalls.
{"title":"Stator fault diagnosis by reactive power in dual three-phase reluctance motors","authors":"C. Bianchini, A. Torreggiani, M. Davoli, A. Bellini, Cristian Babetto, N. Bianchi","doi":"10.1109/DEMPED.2019.8864909","DOIUrl":"https://doi.org/10.1109/DEMPED.2019.8864909","url":null,"abstract":"Electrical machines are wide spread because of their intrinsic robustness, versatility and reduced impact on energy and resources. Recently, the demand has focused on specific features: compatibility with power converters and fault tolerance. In fact, a wide range of applications require variable speed drives and high rejection of faults, i.e. safe operation also for non-critical applications. Here, a dual three-phase stator configuration is used, and a novel method for stator fault detection is presented. This method is based on reactive power measurements from both three-phase systems. A differential diagnostic index is defined, that can be used to detect effectively stator faults, isolating them from torque oscillations, load unbalances or other pitfalls.","PeriodicalId":397001,"journal":{"name":"2019 IEEE 12th International Symposium on Diagnostics for Electrical Machines, Power Electronics and Drives (SDEMPED)","volume":"36 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":"125037905","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}
Pub Date : 2019-08-01DOI: 10.1109/DEMPED.2019.8864920
I. Bouchareb, A. Lebaroud, A. Cardoso, S. Lee
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.
在不久的将来，人工智能(AI)有望成为工业应用竞争力的重要推动力。感应电动机(IMs)在世界范围内被用作工业应用中的“主力”。本文综述了将人工智能技术集成到异步电动机状态监测和故障诊断的可能性。重点研究了异步电动机传动偏心故障的识别、分类和预测的先进诊断方法。转子偏心率一直是许多研究者的研究目标。然而，如果偏心故障单独出现，仍然无法可靠地检测和准确预测。针对这种情况，将神经网络和隐马尔可夫模型(NN-HMM)融合为一个智能诊断系统来克服偏心诊断的不足。基于非参数时频表示(TFR)的电流测量用于特征提取。然后，采用Fisher’s Discriminant Ratio (FDR)特征选择方法，选取最优数量的提取特征，结合多项式方法对各种偏心故障类型和程度进行精确跟踪识别。通过对7.5h异步电动机的实验研究，验证了该方法对不同程度的偏心量(0%、20%、40%、60%、80%)进行状态监测的可靠性和有效性，这些偏心量与负载或电机类型完全无关。
{"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}
Pub Date : 2019-08-01DOI: 10.1109/DEMPED.2019.8864836
Fan Li, Song Qiu, M. Jennings, P. Mawby
MOS capacitors with thick (≈65nm) SiO2 gate oxide were fabricated on 3C-SiC/Si substrates and characterised (CV and IV) at room temperature to study the state of the art 3C-SiC/SiO2 interface. A low interface trap density of ~2.5×1011cm−2eV−1was obtained on N2O annealed devices using the high-low method. Gate oxide was biased with elevated voltage and the distribution of cumulative failed devices was studied. Two failure mechanisms were identified with mechanism 1 dominating the 6-8.5MV/cm range, and mechanism 2 becoming more obvious above S.5MV/cm. The failure rate of fabricated MOS capacitors with a diameter of 100µm at 3MV/cm and room temperature was estimated to be ~3450 PPM.
{"title":"Fabrication and Dielectric Breakdown of 3C-SiC/SiO2 MOS Capacitors","authors":"Fan Li, Song Qiu, M. Jennings, P. Mawby","doi":"10.1109/DEMPED.2019.8864836","DOIUrl":"https://doi.org/10.1109/DEMPED.2019.8864836","url":null,"abstract":"MOS capacitors with thick (≈65nm) SiO2 gate oxide were fabricated on 3C-SiC/Si substrates and characterised (CV and IV) at room temperature to study the state of the art 3C-SiC/SiO2 interface. A low interface trap density of ~2.5×1011cm−2eV−1was obtained on N2O annealed devices using the high-low method. Gate oxide was biased with elevated voltage and the distribution of cumulative failed devices was studied. Two failure mechanisms were identified with mechanism 1 dominating the 6-8.5MV/cm range, and mechanism 2 becoming more obvious above S.5MV/cm. The failure rate of fabricated MOS capacitors with a diameter of 100µm at 3MV/cm and room temperature was estimated to be ~3450 PPM.","PeriodicalId":397001,"journal":{"name":"2019 IEEE 12th International Symposium on Diagnostics for Electrical Machines, Power Electronics and Drives (SDEMPED)","volume":"17 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":"131722958","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}
Pub Date : 2019-08-01DOI: 10.1109/DEMPED.2019.8864804
Amit K. Tiwari, S. Perkin, N. Lophitis, Marina Antoniou, T. Trajkovic, F. Udrea
The robustness of ultra-high voltage (>10kV) SiC IGBTs comprising of an optimized retrograde p-well is investigated. Under extensive TCAD simulations, we show that in addition to offering a robust control on threshold voltage and eliminating punch-through, the retrograde is highly effective in terms of reducing the stress on the gate oxide of ultra-high voltage SiC IGBTs. We show that a 10 kV SiC IGBT comprising of the retrograde p-well exhibits a much-reduced peak electric field in the gate oxide when compared with the counterpart comprising of a conventional p-well. Using an optimized retrograde p-well with depth as shallow as 1 µm, the peak electric field in the gate oxide of a 10kV rated SiC IGBT can be reduced to below 2 MV.cm−1, a prerequisite to achieve a high-degree of reliability in high-voltage power devices. We therefore propose that the retrograde p-well is highly promising for the development of>10kV SiC IGBTs.
研究了由优化的逆行p井组成的超高压(>10kV) SiC igbt的鲁棒性。在广泛的TCAD模拟中，我们表明，除了提供对阈值电压的鲁棒控制和消除穿孔外，逆行在减少超高压SiC igbt栅极氧化物上的应力方面非常有效。我们表明，与由传统p阱组成的对应物相比，由逆行p阱组成的10 kV SiC IGBT在栅极氧化物中显示出大大降低的峰值电场。采用优化后的深度为1 μ m的逆行p井，10kV额定SiC IGBT栅极氧化物中的峰值电场可降至2 MV以下。Cm−1，是实现高压电源器件高可靠性的前提条件。因此，我们提出逆行p井对于>10kV SiC igbt的开发具有很大的前景。
{"title":"On the robustness of ultra-high voltage 4H-SiC IGBTs with an optimized retrograde p-well","authors":"Amit K. Tiwari, S. Perkin, N. Lophitis, Marina Antoniou, T. Trajkovic, F. Udrea","doi":"10.1109/DEMPED.2019.8864804","DOIUrl":"https://doi.org/10.1109/DEMPED.2019.8864804","url":null,"abstract":"The robustness of ultra-high voltage (>10kV) SiC IGBTs comprising of an optimized retrograde p-well is investigated. Under extensive TCAD simulations, we show that in addition to offering a robust control on threshold voltage and eliminating punch-through, the retrograde is highly effective in terms of reducing the stress on the gate oxide of ultra-high voltage SiC IGBTs. We show that a 10 kV SiC IGBT comprising of the retrograde p-well exhibits a much-reduced peak electric field in the gate oxide when compared with the counterpart comprising of a conventional p-well. Using an optimized retrograde p-well with depth as shallow as 1 µm, the peak electric field in the gate oxide of a 10kV rated SiC IGBT can be reduced to below 2 MV.cm−1, a prerequisite to achieve a high-degree of reliability in high-voltage power devices. We therefore propose that the retrograde p-well is highly promising for the development of>10kV SiC IGBTs.","PeriodicalId":397001,"journal":{"name":"2019 IEEE 12th International Symposium on Diagnostics for Electrical Machines, Power Electronics and Drives (SDEMPED)","volume":"24 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":"133256152","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}
Pub Date : 2019-08-01DOI: 10.1109/DEMPED.2019.8864831
Mengshi Li, Yaozhou Yu, T. Ji, Qinghua Wu
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.
{"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}
Pub Date : 2019-08-01DOI: 10.1109/DEMPED.2019.8864852
E. Pošković, L. Ferraris, G. Bramerdorfer, M. Cossale
Ferromagnetic materials may be affected by the presence of local losses due to defects or magnetic anomalies caused by machining processes. To highlight such anomalies is absolutely not easy; a non invasive thermographic method has been refined to allow a proper comparison of different machining processes impact on the iron losses. Specimens obtained with punching, wire erosion and laser cut have been analyzed by means of a high speed IR camera when subjected to alternate magnetization at different frequencies. The possibility to point out localized anomalies should be exploited to foresee and avoid electrical machines core faults.
{"title":"A Thermographic Method to Evaluate Different Processes Effects on Magnetic Steels","authors":"E. Pošković, L. Ferraris, G. Bramerdorfer, M. Cossale","doi":"10.1109/DEMPED.2019.8864852","DOIUrl":"https://doi.org/10.1109/DEMPED.2019.8864852","url":null,"abstract":"Ferromagnetic materials may be affected by the presence of local losses due to defects or magnetic anomalies caused by machining processes. To highlight such anomalies is absolutely not easy; a non invasive thermographic method has been refined to allow a proper comparison of different machining processes impact on the iron losses. Specimens obtained with punching, wire erosion and laser cut have been analyzed by means of a high speed IR camera when subjected to alternate magnetization at different frequencies. The possibility to point out localized anomalies should be exploited to foresee and avoid electrical machines core faults.","PeriodicalId":397001,"journal":{"name":"2019 IEEE 12th International Symposium on Diagnostics for Electrical Machines, Power Electronics and Drives (SDEMPED)","volume":"35 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":"116645512","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}
Pub Date : 2019-08-01DOI: 10.1109/DEMPED.2019.8864872
D. Martinez, H. Henao, G. Capolino
This paper provides a comprehensive survey on the state-of-the-art of condition monitoring technologies as enabling the fault detection for the power distribution grid. Several engineering efforts have already been initiated to modernize the power grid, but in most cases, the increasing complexity of distribution systems has become a major topic for monitoring techniques, and even the diagnostic methods are not suitable to assess the system behavior progress. This work aims to review existing literature surveys on real-time systems in the context of monitoring and fault diagnosis applied to electric distribution systems. Moreover, this paper summarizes some realtime applications and its evolution regarding the design, the analysis, and the testing for a deeper understanding of interface issues. A brief description of the future challenges of real-time simulation tools applied to condition monitoring is introduced as well.
{"title":"Overview of Condition Monitoring Systems for Power Distribution Grids","authors":"D. Martinez, H. Henao, G. Capolino","doi":"10.1109/DEMPED.2019.8864872","DOIUrl":"https://doi.org/10.1109/DEMPED.2019.8864872","url":null,"abstract":"This paper provides a comprehensive survey on the state-of-the-art of condition monitoring technologies as enabling the fault detection for the power distribution grid. Several engineering efforts have already been initiated to modernize the power grid, but in most cases, the increasing complexity of distribution systems has become a major topic for monitoring techniques, and even the diagnostic methods are not suitable to assess the system behavior progress. This work aims to review existing literature surveys on real-time systems in the context of monitoring and fault diagnosis applied to electric distribution systems. Moreover, this paper summarizes some realtime applications and its evolution regarding the design, the analysis, and the testing for a deeper understanding of interface issues. A brief description of the future challenges of real-time simulation tools applied to condition monitoring is introduced as well.","PeriodicalId":397001,"journal":{"name":"2019 IEEE 12th International Symposium on Diagnostics for Electrical Machines, Power Electronics and Drives (SDEMPED)","volume":"13 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":"124869448","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}
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