Pub Date : 2019-08-01DOI: 10.1109/DEMPED.2019.8864873
W. R. Jensen, Shanelle N. Foster
Faults in electrical machines or their drives lead to degraded performance and, in some cases, unsafe operating conditions. MOSFET devices in an inverter-drive allow for higher switching frequencies. However, both power MOSFETs and Silicon Carbide MOSFETs experience degradation in the insulating gate oxide layer from excess voltage or temperature. Indicators of gate oxide degradation are measurable, but many require access to the leads of the device and additional voltage or current sensors. For an inverter-drive application, current sensors are commonly employed for controlling the machine. Detecting gate oxide degradation in the measured phase currents is noninvasive and can be performed online. In this work, degradation of gate oxide is performed in power MOSFETs and the corresponding changes in current transient waveforms are quantified.
{"title":"Online MOSFET Condition Monitoring for Inverter-Driven Electric Machines","authors":"W. R. Jensen, Shanelle N. Foster","doi":"10.1109/DEMPED.2019.8864873","DOIUrl":"https://doi.org/10.1109/DEMPED.2019.8864873","url":null,"abstract":"Faults in electrical machines or their drives lead to degraded performance and, in some cases, unsafe operating conditions. MOSFET devices in an inverter-drive allow for higher switching frequencies. However, both power MOSFETs and Silicon Carbide MOSFETs experience degradation in the insulating gate oxide layer from excess voltage or temperature. Indicators of gate oxide degradation are measurable, but many require access to the leads of the device and additional voltage or current sensors. For an inverter-drive application, current sensors are commonly employed for controlling the machine. Detecting gate oxide degradation in the measured phase currents is noninvasive and can be performed online. In this work, degradation of gate oxide is performed in power MOSFETs and the corresponding changes in current transient waveforms are quantified.","PeriodicalId":397001,"journal":{"name":"2019 IEEE 12th International Symposium on Diagnostics for Electrical Machines, Power Electronics and Drives (SDEMPED)","volume":"139 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":"127015090","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.2019.8864866
F. Immovilli, Marco Lippi, M. Cocconcelli
This paper presents a method for automated bearing fault detection via motor current analysis using Long Short-Term Memory networks. Minimal pre-processing is applied to current signals. The proposed approach is experimentally validated on a laboratory trial comprising different test sets for condition monitoring and fault diagnosis of a 6-poles induction motor. Preliminary results confirmed the effectiveness of the proposed method to detect various bearing faults under different operating conditions, such as: shaft radial load and output torque.
{"title":"Automated Bearing Fault Detection via Long Short-Term Memory Networks","authors":"F. Immovilli, Marco Lippi, M. Cocconcelli","doi":"10.1109/DEMPED.2019.8864866","DOIUrl":"https://doi.org/10.1109/DEMPED.2019.8864866","url":null,"abstract":"This paper presents a method for automated bearing fault detection via motor current analysis using Long Short-Term Memory networks. Minimal pre-processing is applied to current signals. The proposed approach is experimentally validated on a laboratory trial comprising different test sets for condition monitoring and fault diagnosis of a 6-poles induction motor. Preliminary results confirmed the effectiveness of the proposed method to detect various bearing faults under different operating conditions, such as: shaft radial load and output torque.","PeriodicalId":397001,"journal":{"name":"2019 IEEE 12th International Symposium on Diagnostics for Electrical Machines, Power Electronics and Drives (SDEMPED)","volume":"1 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":"130560538","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.2019.8864830
A. Guedidi, A. Guettaf, A. Cardoso, W. Laala, A. Arif
Bearing fault is the most causes of machine breakdowns. Consequently, the monitoring of this component is a key point to increase the reliability, security and avoiding serious damage in machine. Vibration signal is widely used for diagnosis which is considered as a powerful tool for detecting mechanical defects. In this paper, a rolling bearing fault-diagnosis method based on variational mode decomposition (VMD) and artificial neural network (ANN) is proposed. First, the processing methodology of bearing diagnosis starts with the decomposition of the vibration signal by VMD technique into a set of intrinsic mode functions (IMFs). According to the aim of fault diagnosis, the selected fault indicator is calculated from the energy related to the most sensitive IMFs to the bearing defect. Second, the extracted feature is then used as input to the ANN. the proposed approach is then validated using data from the bearing data center of Case Western Reserve University. The results prove the efficient of this method which is able to discriminating from four conditions of rolling bearing, namely, normal bearing and three different types of defected bearings: outer race, inner race, and ball.
轴承故障是造成机器故障最多的原因。因此，对该部件的监控是提高设备可靠性、安全性和避免严重损坏的关键。振动信号被广泛用于诊断，被认为是检测机械缺陷的有力工具。提出了一种基于变分模态分解(VMD)和人工神经网络(ANN)的滚动轴承故障诊断方法。首先，轴承诊断的处理方法是从VMD技术将振动信号分解为一组内禀模态函数(IMFs)开始的。根据故障诊断的目的，从轴承缺陷最敏感的imf的相关能量中计算所选择的故障指标。其次，将提取的特征用作人工神经网络的输入。然后使用凯斯西储大学(Case Western Reserve University)方位数据中心的数据对所提出的方法进行了验证。结果证明了该方法的有效性，该方法能够对滚动轴承的四种状态即正常轴承和三种不同类型的缺陷轴承(外滚圈、内滚圈和球)进行识别。
{"title":"Bearing Faults Classification Based on Variational Mode Decomposition and Artificial Neural Network","authors":"A. Guedidi, A. Guettaf, A. Cardoso, W. Laala, A. Arif","doi":"10.1109/DEMPED.2019.8864830","DOIUrl":"https://doi.org/10.1109/DEMPED.2019.8864830","url":null,"abstract":"Bearing fault is the most causes of machine breakdowns. Consequently, the monitoring of this component is a key point to increase the reliability, security and avoiding serious damage in machine. Vibration signal is widely used for diagnosis which is considered as a powerful tool for detecting mechanical defects. In this paper, a rolling bearing fault-diagnosis method based on variational mode decomposition (VMD) and artificial neural network (ANN) is proposed. First, the processing methodology of bearing diagnosis starts with the decomposition of the vibration signal by VMD technique into a set of intrinsic mode functions (IMFs). According to the aim of fault diagnosis, the selected fault indicator is calculated from the energy related to the most sensitive IMFs to the bearing defect. Second, the extracted feature is then used as input to the ANN. the proposed approach is then validated using data from the bearing data center of Case Western Reserve University. The results prove the efficient of this method which is able to discriminating from four conditions of rolling bearing, namely, normal bearing and three different types of defected bearings: outer race, inner race, and ball.","PeriodicalId":397001,"journal":{"name":"2019 IEEE 12th International Symposium on Diagnostics for Electrical Machines, Power Electronics and Drives (SDEMPED)","volume":"27 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":"131714265","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.2019.8864851
Dimitrios A. Papathanasopoulos, E. Mitronikas
In this study, a robust fault diagnostic technique, based on the virtual third harmonic Back-EMF, is proposed to identify the Hall-effect position sensor misalignment in Brushless DC (BLDC) motor drives. The proposed technique can also be exploited in sensorless BLDC drives to highlight the commutation errors and, consequently, the unbalanced operation of a BLDC motor drive, either sensor-based or not, can be detected. Therefore, potential signals, which incorporate the virtual third harmonic Back-EMF, are investigated in the frequency domain for a reliable diagnosis of the defective system. Through the comparison of the pros and cons of each signal, the voltage difference between the neutral point of a resistor network and the half of the DC-link is proposed for the development of the diagnostic technique. Thus, this signal is evaluated in identifying both the unbalanced system operation and the severity of the defect in a wide speed and load range.
{"title":"Evaluation of the Virtual Third Harmonic Back-EMF in Identifying Misaligned Hall-effect Position Sensors in Brushless DC Motor Drives","authors":"Dimitrios A. Papathanasopoulos, E. Mitronikas","doi":"10.1109/DEMPED.2019.8864851","DOIUrl":"https://doi.org/10.1109/DEMPED.2019.8864851","url":null,"abstract":"In this study, a robust fault diagnostic technique, based on the virtual third harmonic Back-EMF, is proposed to identify the Hall-effect position sensor misalignment in Brushless DC (BLDC) motor drives. The proposed technique can also be exploited in sensorless BLDC drives to highlight the commutation errors and, consequently, the unbalanced operation of a BLDC motor drive, either sensor-based or not, can be detected. Therefore, potential signals, which incorporate the virtual third harmonic Back-EMF, are investigated in the frequency domain for a reliable diagnosis of the defective system. Through the comparison of the pros and cons of each signal, the voltage difference between the neutral point of a resistor network and the half of the DC-link is proposed for the development of the diagnostic technique. Thus, this signal is evaluated in identifying both the unbalanced system operation and the severity of the defect in a wide speed and load range.","PeriodicalId":397001,"journal":{"name":"2019 IEEE 12th International Symposium on Diagnostics for Electrical Machines, Power Electronics and Drives (SDEMPED)","volume":"67 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":"132013237","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.2019.8864878
J. Bonet-Jara, J. Pons-Llinares
In induction motors diagnosis, sensorless speed estimation has become a key factor when it comes to locate fault harmonics. Yet, there are some issues that have not been solved. First, since methods were mainly thought to control purposes, they are intended to provide real time response, which means short data records. In FFT techniques (widely used in fault diagnosis) those short data records mean low accuracy. Second, most methods require specific machine parameters that are neither available, nor easily estimated. The present paper is a state of the art review on sensorless speed estimation. It is intended to be a guide for industrial professionals where they may find which method fits better their problems. Moreover, it also analyzes and points out the current problems of using sensorless speed estimation in fault diagnosis to indicate future lines of research where academia could focus their efforts to finish solving the problem.
{"title":"Sensorlees Speed Estimation. A Review","authors":"J. Bonet-Jara, J. Pons-Llinares","doi":"10.1109/DEMPED.2019.8864878","DOIUrl":"https://doi.org/10.1109/DEMPED.2019.8864878","url":null,"abstract":"In induction motors diagnosis, sensorless speed estimation has become a key factor when it comes to locate fault harmonics. Yet, there are some issues that have not been solved. First, since methods were mainly thought to control purposes, they are intended to provide real time response, which means short data records. In FFT techniques (widely used in fault diagnosis) those short data records mean low accuracy. Second, most methods require specific machine parameters that are neither available, nor easily estimated. The present paper is a state of the art review on sensorless speed estimation. It is intended to be a guide for industrial professionals where they may find which method fits better their problems. Moreover, it also analyzes and points out the current problems of using sensorless speed estimation in fault diagnosis to indicate future lines of research where academia could focus their efforts to finish solving the problem.","PeriodicalId":397001,"journal":{"name":"2019 IEEE 12th International Symposium on Diagnostics for Electrical Machines, Power Electronics and Drives (SDEMPED)","volume":"37 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":"131254536","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.2019.8864853
Pengfei Tian, C. Platero, K. Gyftakis
Turn-to-turn faults are quite common in the synchronous generator field winding, especially in the turbogenerator types. This condition may be caused by either the double- ground fault of the winding or by a lack of insulation between adjacent turns. This paper presents a new on-line protection method for turn-to-turn faults in field windings of synchronous machines with static excitation system. The proposed method is based on the comparison of the measured excitation current with the theoretical excitation current calculated from the stator voltages and currents for the actual operation point. The method has been validated by experimental results using a special laboratory synchronous machine.
{"title":"On-line Turn-to-Turn Protection Method of the Synchronous Machines Field Winding","authors":"Pengfei Tian, C. Platero, K. Gyftakis","doi":"10.1109/DEMPED.2019.8864853","DOIUrl":"https://doi.org/10.1109/DEMPED.2019.8864853","url":null,"abstract":"Turn-to-turn faults are quite common in the synchronous generator field winding, especially in the turbogenerator types. This condition may be caused by either the double- ground fault of the winding or by a lack of insulation between adjacent turns. This paper presents a new on-line protection method for turn-to-turn faults in field windings of synchronous machines with static excitation system. The proposed method is based on the comparison of the measured excitation current with the theoretical excitation current calculated from the stator voltages and currents for the actual operation point. The method has been validated by experimental results using a special laboratory synchronous machine.","PeriodicalId":397001,"journal":{"name":"2019 IEEE 12th International Symposium on Diagnostics for Electrical Machines, Power Electronics and Drives (SDEMPED)","volume":"70 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":"121209546","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.2019.8864846
V. Fernandez-Cavero, J. Pons-Llinares, Ó. Duque-Pérez, D. Morinigo-Sotelo
Fault detection in inverter-fed induction motors operating in non-stationary regimes it is still a challenge. In the case of rotor bar breakages, the fault-related harmonics evolve in the time-frequency plane very close to the fundamental component, and with much lower amplitudes than the fundamental. These two facts make their observation difficult. The Dragon Transform here presented is developed to solve this problem. In this paper, this transform is tested with non-linear standard inverter-fed startups, where, thanks to its high time-frequency resolution, is capable of detecting fault harmonics even with evolutions extremely close to the main component.
{"title":"Detection of broken rotor bars in non-linear startups of inverter-fed induction motors","authors":"V. Fernandez-Cavero, J. Pons-Llinares, Ó. Duque-Pérez, D. Morinigo-Sotelo","doi":"10.1109/DEMPED.2019.8864846","DOIUrl":"https://doi.org/10.1109/DEMPED.2019.8864846","url":null,"abstract":"Fault detection in inverter-fed induction motors operating in non-stationary regimes it is still a challenge. In the case of rotor bar breakages, the fault-related harmonics evolve in the time-frequency plane very close to the fundamental component, and with much lower amplitudes than the fundamental. These two facts make their observation difficult. The Dragon Transform here presented is developed to solve this problem. In this paper, this transform is tested with non-linear standard inverter-fed startups, where, thanks to its high time-frequency resolution, is capable of detecting fault harmonics even with evolutions extremely close to the main component.","PeriodicalId":397001,"journal":{"name":"2019 IEEE 12th International Symposium on Diagnostics for Electrical Machines, Power Electronics and Drives (SDEMPED)","volume":"1 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":"125820843","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.2019.8864870
T. S. Wang, T. Ji, M. S. Li
Non-intrusive load monitoring (NILM) is a task of estimating the contribution of individual appliance to the overall power consumption by using a set of electrical signals measured by a smart meter. In this paper, we propose a comprehensive and extensible framework based on DNNs. We employ denoising autoencoder (dAE) to reconstruct the power signal of individual appliance from aggregated power consumption, and we use long short term memory (LSTM) network to make sure which appliance the power signal belongs to. We select 5 appliances to validate our method, and the results have shown the advantages of the proposed framework in some aspects compared to hidden Markov models (HMMs) and premier dAE.
{"title":"A New Approach for Supervised Power Disaggregation by Using a Denoising Autoencoder and Recurrent LSTM Network","authors":"T. S. Wang, T. Ji, M. S. Li","doi":"10.1109/DEMPED.2019.8864870","DOIUrl":"https://doi.org/10.1109/DEMPED.2019.8864870","url":null,"abstract":"Non-intrusive load monitoring (NILM) is a task of estimating the contribution of individual appliance to the overall power consumption by using a set of electrical signals measured by a smart meter. In this paper, we propose a comprehensive and extensible framework based on DNNs. We employ denoising autoencoder (dAE) to reconstruct the power signal of individual appliance from aggregated power consumption, and we use long short term memory (LSTM) network to make sure which appliance the power signal belongs to. We select 5 appliances to validate our method, and the results have shown the advantages of the proposed framework in some aspects compared to hidden Markov models (HMMs) and premier dAE.","PeriodicalId":397001,"journal":{"name":"2019 IEEE 12th International Symposium on Diagnostics for Electrical Machines, Power Electronics and Drives (SDEMPED)","volume":"241 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":"130679596","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.2019.8864908
Anmol Aggarwal, E. Strangas, J. Agapiou
This paper proposes and compares detection methods for the presence of static eccentricity in Interior Permanent Magnet Synchronous Machines (IPMSM). Three methods are proposed. The first method is based on commanded voltages that uses shift in the voltages in d-q plane to detect fault. The second method is based on the incremental inductance that uses shift in peak of the curve for fault detection. The third method uses the combined information present both in current and voltage harmonics to detect the fault. This makes the detection scheme robust with respect to current controller bandwidth. It is shown that all three methods are capable of detecting presence of static eccentricity. For all three methods the machine was tested at healthy, 25% and 50% static eccentricity levels. The machine was controlled using Field Oriented Control (FOC) using Real Time LABVIEW. Two dimensional (2-D) Finite Element Analysis (FEA) was used to model and simulate the machine under healthy and faulty conditions to evaluate the results from experiments.
{"title":"Comparative Study of Offline Detection Methods of Static Eccentricity for Interior Permanent Magnet Synchronous Machine","authors":"Anmol Aggarwal, E. Strangas, J. Agapiou","doi":"10.1109/DEMPED.2019.8864908","DOIUrl":"https://doi.org/10.1109/DEMPED.2019.8864908","url":null,"abstract":"This paper proposes and compares detection methods for the presence of static eccentricity in Interior Permanent Magnet Synchronous Machines (IPMSM). Three methods are proposed. The first method is based on commanded voltages that uses shift in the voltages in d-q plane to detect fault. The second method is based on the incremental inductance that uses shift in peak of the curve for fault detection. The third method uses the combined information present both in current and voltage harmonics to detect the fault. This makes the detection scheme robust with respect to current controller bandwidth. It is shown that all three methods are capable of detecting presence of static eccentricity. For all three methods the machine was tested at healthy, 25% and 50% static eccentricity levels. The machine was controlled using Field Oriented Control (FOC) using Real Time LABVIEW. Two dimensional (2-D) Finite Element Analysis (FEA) was used to model and simulate the machine under healthy and faulty conditions to evaluate the results from experiments.","PeriodicalId":397001,"journal":{"name":"2019 IEEE 12th International Symposium on Diagnostics for Electrical Machines, Power Electronics and Drives (SDEMPED)","volume":"94 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":"132756137","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.2019.8864867
Lixin Wu, Tongzhen Wei, Changli Shi, Jingyuan Yin
Because of the sub-modules fault, redundant sub-modules may be exhausted in the long-term operation of modular multilevel converter (MMC). In order to ensure that the MMC does not stop running under the extreme conditions of sub-module failure, this paper proposes a novel neutral point offset fault-tolerant control strategy for MMC. This method can maintain the symmetrical operation between the system lines only by injecting the fundamental frequency voltage modulation component, and it is simple to realize. Moreover, it does not need to raise the operating voltage of the faulty phase sub-module, which reduces the voltage stress of the sub-module switching device. Finally, the simulation and experiment are carried out to verify the effectiveness of the proposed fault-tolerant control strategy.
{"title":"Fault-Tolerant Control Strategy for Sub-Module Faults of Modular Multilevel Converters","authors":"Lixin Wu, Tongzhen Wei, Changli Shi, Jingyuan Yin","doi":"10.1109/DEMPED.2019.8864867","DOIUrl":"https://doi.org/10.1109/DEMPED.2019.8864867","url":null,"abstract":"Because of the sub-modules fault, redundant sub-modules may be exhausted in the long-term operation of modular multilevel converter (MMC). In order to ensure that the MMC does not stop running under the extreme conditions of sub-module failure, this paper proposes a novel neutral point offset fault-tolerant control strategy for MMC. This method can maintain the symmetrical operation between the system lines only by injecting the fundamental frequency voltage modulation component, and it is simple to realize. Moreover, it does not need to raise the operating voltage of the faulty phase sub-module, which reduces the voltage stress of the sub-module switching device. Finally, the simulation and experiment are carried out to verify the effectiveness of the proposed fault-tolerant control strategy.","PeriodicalId":397001,"journal":{"name":"2019 IEEE 12th International Symposium on Diagnostics for Electrical Machines, Power Electronics and Drives (SDEMPED)","volume":"1 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":"131217740","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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