Pub Date : 2013-10-24DOI: 10.1109/DEMPED.2013.6645695
H. Razik, M. Oumaamar, G. Clerc
Even if the asynchronous machine is widely used in industrial process under speed variable, we have to monitor the process which is composed of an inverter and a motor in an enclosed space. Thus, the stator current is not accessible and the AC supply line current is only available to monitor the system in comparison to a motor connected directly to the power supply. In this paper, we focus our attention on the tracking of faulty lines rising up in the supply line current due to a rotor bar defect. We suggest to use a meta-heuristic method to tackle this problem. In this paper a hybrid kangaroo and a non-linear great deluge are taken into consideration to track these defective lines thanks to the analysis of the supply line. Experiments results show the effectiveness of this approach.
{"title":"A hybrid kangaroo algorithm to assess the state of health of electric motors","authors":"H. Razik, M. Oumaamar, G. Clerc","doi":"10.1109/DEMPED.2013.6645695","DOIUrl":"https://doi.org/10.1109/DEMPED.2013.6645695","url":null,"abstract":"Even if the asynchronous machine is widely used in industrial process under speed variable, we have to monitor the process which is composed of an inverter and a motor in an enclosed space. Thus, the stator current is not accessible and the AC supply line current is only available to monitor the system in comparison to a motor connected directly to the power supply. In this paper, we focus our attention on the tracking of faulty lines rising up in the supply line current due to a rotor bar defect. We suggest to use a meta-heuristic method to tackle this problem. In this paper a hybrid kangaroo and a non-linear great deluge are taken into consideration to track these defective lines thanks to the analysis of the supply line. Experiments results show the effectiveness of this approach.","PeriodicalId":425644,"journal":{"name":"2013 9th IEEE International Symposium on Diagnostics for Electric Machines, Power Electronics and Drives (SDEMPED)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2013-10-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130677040","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 : 2013-10-24DOI: 10.1109/DEMPED.2013.6645718
P. Rodríguez, P. Rzeszucinski, M. Sułowicz, Rolf Disselnkoetter, U. Ahrend, C. Pinto, J. Ottewill, S. Wildermuth
Often found in critical, high power applications, synchronous machines require reliable condition monitoring systems. Large synchronous machines are typically designed with parallel connected windings in order to split the currents in parallel paths, delivering the total power at the terminals. Under ideal symmetrical conditions, no current will circulate between parallel branches of the same phase. However, when a motor fault breaks this symmetry, currents circulate between the branches. Thus, due to the fact that they are only non-zero under faulty conditions, circulating currents potentially represent a sensitive indicator of faulty condition. In this paper, the advantages of using the circulating current between parallel branches of the stator of a synchronous motor as an early indicator of motor faults are shown. Analysis is conducted both through simulation, via the use of finite element methods (FEM), and through experimentation using a specially-designed synchronous machine which allows various fault conditions to be investigated. Through comparison between experiment and simulation, the simulation tool is validated. Furthermore, it is shown that the circulating current is better suited for fault detection than either the branch or the stator current. It is concluded that an improved condition monitoring and protection system for a synchronous machine may be achieved if these currents are monitored.
{"title":"Stator circulating currents as media of fault detection in synchronous motors","authors":"P. Rodríguez, P. Rzeszucinski, M. Sułowicz, Rolf Disselnkoetter, U. Ahrend, C. Pinto, J. Ottewill, S. Wildermuth","doi":"10.1109/DEMPED.2013.6645718","DOIUrl":"https://doi.org/10.1109/DEMPED.2013.6645718","url":null,"abstract":"Often found in critical, high power applications, synchronous machines require reliable condition monitoring systems. Large synchronous machines are typically designed with parallel connected windings in order to split the currents in parallel paths, delivering the total power at the terminals. Under ideal symmetrical conditions, no current will circulate between parallel branches of the same phase. However, when a motor fault breaks this symmetry, currents circulate between the branches. Thus, due to the fact that they are only non-zero under faulty conditions, circulating currents potentially represent a sensitive indicator of faulty condition. In this paper, the advantages of using the circulating current between parallel branches of the stator of a synchronous motor as an early indicator of motor faults are shown. Analysis is conducted both through simulation, via the use of finite element methods (FEM), and through experimentation using a specially-designed synchronous machine which allows various fault conditions to be investigated. Through comparison between experiment and simulation, the simulation tool is validated. Furthermore, it is shown that the circulating current is better suited for fault detection than either the branch or the stator current. It is concluded that an improved condition monitoring and protection system for a synchronous machine may be achieved if these currents are monitored.","PeriodicalId":425644,"journal":{"name":"2013 9th IEEE International Symposium on Diagnostics for Electric Machines, Power Electronics and Drives (SDEMPED)","volume":"34 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2013-10-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115094054","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 : 2013-10-24DOI: 10.1109/DEMPED.2013.6645742
C. Harlisca, L. Szabó, L. Frosini, A. Albini
Frequent defects of induction machines are due to diverse bearing faults. The detection of such faults in their incipient phase can decisively contribute to the prevention of unplanned breakdowns in industrial plants. In this paper the detection of three types of bearing faults by means of statistical processing of the stray fluxes measurements is detailed. The developed noninvasive method requires only both simple probes and easy computations. Numerous measurements had been performed for all the combinations of bearing faults, loads and stray flux probes taken into study. All the results emphasized the effectiveness of the applied simple fault diagnosis method.
{"title":"Bearing faults detection in induction machines based on statistical processing of the stray fluxes measurements","authors":"C. Harlisca, L. Szabó, L. Frosini, A. Albini","doi":"10.1109/DEMPED.2013.6645742","DOIUrl":"https://doi.org/10.1109/DEMPED.2013.6645742","url":null,"abstract":"Frequent defects of induction machines are due to diverse bearing faults. The detection of such faults in their incipient phase can decisively contribute to the prevention of unplanned breakdowns in industrial plants. In this paper the detection of three types of bearing faults by means of statistical processing of the stray fluxes measurements is detailed. The developed noninvasive method requires only both simple probes and easy computations. Numerous measurements had been performed for all the combinations of bearing faults, loads and stray flux probes taken into study. All the results emphasized the effectiveness of the applied simple fault diagnosis method.","PeriodicalId":425644,"journal":{"name":"2013 9th IEEE International Symposium on Diagnostics for Electric Machines, Power Electronics and Drives (SDEMPED)","volume":"2 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2013-10-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114282998","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 : 2013-10-24DOI: 10.1109/DEMPED.2013.6645767
H. L. Schmitt, L. R. B. Silva, P. Scalassara, A. Goedtel
Fault detection in electrical machines have been widely explored by researchers, especially bearing faults that represents about 40% to 60% of the total faults. Since this kind of fault is detectable by particular frequencies at the stator current, it is now a source of investigation. Thus, this work presents a predicability analysis method based on relative entropy measures estimated over reconstructed signals obtained from wavelet-packet decomposition components. The signals were simulated using a real motor current signal with addition of frequency components related to the bearing faults. Using three ANN topologies, these entropy measures are classified in two groups: normal and faulty signals with a high performance rate.
{"title":"Bearing fault detection using relative entropy of wavelet components and artificial neural networks","authors":"H. L. Schmitt, L. R. B. Silva, P. Scalassara, A. Goedtel","doi":"10.1109/DEMPED.2013.6645767","DOIUrl":"https://doi.org/10.1109/DEMPED.2013.6645767","url":null,"abstract":"Fault detection in electrical machines have been widely explored by researchers, especially bearing faults that represents about 40% to 60% of the total faults. Since this kind of fault is detectable by particular frequencies at the stator current, it is now a source of investigation. Thus, this work presents a predicability analysis method based on relative entropy measures estimated over reconstructed signals obtained from wavelet-packet decomposition components. The signals were simulated using a real motor current signal with addition of frequency components related to the bearing faults. Using three ANN topologies, these entropy measures are classified in two groups: normal and faulty signals with a high performance rate.","PeriodicalId":425644,"journal":{"name":"2013 9th IEEE International Symposium on Diagnostics for Electric Machines, Power Electronics and Drives (SDEMPED)","volume":"6 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2013-10-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128243837","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 : 2013-10-24DOI: 10.1109/DEMPED.2013.6645715
K. H. Lee, Jongman Hong, D. Hyun, Sang Bin Lee, E. Wiedenbrug, M. Teska, Chaewoon Lim
The recent trend in large ac machines is to employ magnetic stator slot wedges for improving the motor efficiency, power factor, and power density. The mechanical strength of magnetic wedges is weak compared to the epoxy glass wedges, and many cases of loose and missing wedges have recently been increasingly reported. Magnetic wedge failure deteriorates the performance and reliability of the motor, but there is no method available for testing the wedge quality other than visual inspection after rotor removal. Monitoring of overall wedge condition without motor disassembly can help reduce the cost of maintenance and risk of degradation in performance. In this paper, a new off-line standstill test method for detecting magnetic wedge problems for ac machines without motor disassembly is proposed. An experimental study on 380 V, 5.5 kW and 6.6 kV, 3.4 MW motors with magnetic wedges is performed to verify the effectiveness of the new test method. It is shown that the new method can provide reliable monitoring of magnetic wedge problems over time, independent of other faults or motor design.
{"title":"Detection of stator slot magnetic wedge failures for induction motors without disassembly","authors":"K. H. Lee, Jongman Hong, D. Hyun, Sang Bin Lee, E. Wiedenbrug, M. Teska, Chaewoon Lim","doi":"10.1109/DEMPED.2013.6645715","DOIUrl":"https://doi.org/10.1109/DEMPED.2013.6645715","url":null,"abstract":"The recent trend in large ac machines is to employ magnetic stator slot wedges for improving the motor efficiency, power factor, and power density. The mechanical strength of magnetic wedges is weak compared to the epoxy glass wedges, and many cases of loose and missing wedges have recently been increasingly reported. Magnetic wedge failure deteriorates the performance and reliability of the motor, but there is no method available for testing the wedge quality other than visual inspection after rotor removal. Monitoring of overall wedge condition without motor disassembly can help reduce the cost of maintenance and risk of degradation in performance. In this paper, a new off-line standstill test method for detecting magnetic wedge problems for ac machines without motor disassembly is proposed. An experimental study on 380 V, 5.5 kW and 6.6 kV, 3.4 MW motors with magnetic wedges is performed to verify the effectiveness of the new test method. It is shown that the new method can provide reliable monitoring of magnetic wedge problems over time, independent of other faults or motor design.","PeriodicalId":425644,"journal":{"name":"2013 9th IEEE International Symposium on Diagnostics for Electric Machines, Power Electronics and Drives (SDEMPED)","volume":"78 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2013-10-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"134606234","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 : 2013-10-24DOI: 10.1109/DEMPED.2013.6645723
M. Rodríguez-Blanco, A. Vazquez-Perez, L. Hernández-González, A. Pech-Carbonell, M. May-Alarcon
This paper presents the design of an electronic fault detection circuit in the insulated gate bipolar transistor (IGBT) based on the exclusive measurement of the gate signal during the turn-on transient. In order to increase the effectiveness of the detection and to tolerate the variations of input to system, adaptable thresholds have been added to the circuit. There are three important aspects in this research, specifically: 1 - Early detection, since the evaluation is realized during the turn-on transient; 2 - Reducing false alarms, because the variations of input to the system are considered; 3 - A realistic design, since the components used are commercially available, and the IGBT model used is the standard for PSpice software which has already been widely validated in the literature.
{"title":"IGBT fault diagnosis using adaptive thresholds during the turn-on transient","authors":"M. Rodríguez-Blanco, A. Vazquez-Perez, L. Hernández-González, A. Pech-Carbonell, M. May-Alarcon","doi":"10.1109/DEMPED.2013.6645723","DOIUrl":"https://doi.org/10.1109/DEMPED.2013.6645723","url":null,"abstract":"This paper presents the design of an electronic fault detection circuit in the insulated gate bipolar transistor (IGBT) based on the exclusive measurement of the gate signal during the turn-on transient. In order to increase the effectiveness of the detection and to tolerate the variations of input to system, adaptable thresholds have been added to the circuit. There are three important aspects in this research, specifically: 1 - Early detection, since the evaluation is realized during the turn-on transient; 2 - Reducing false alarms, because the variations of input to the system are considered; 3 - A realistic design, since the components used are commercially available, and the IGBT model used is the standard for PSpice software which has already been widely validated in the literature.","PeriodicalId":425644,"journal":{"name":"2013 9th IEEE International Symposium on Diagnostics for Electric Machines, Power Electronics and Drives (SDEMPED)","volume":"87 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2013-10-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"133178318","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 : 2013-10-24DOI: 10.1109/DEMPED.2013.6645776
Rodney K. Singleton, E. Strangas, Selin Aviyente
Reliable operation of electrical machines depends on the timely detection and diagnosis of faults as well as on prognosis, i.e. estimating the remaining useful life (RUL) of the components. Bearings are the most common components in rotary machines and usually constitute a large portion of the failure cases in these machines. Although there has been a lot of work in the study of bearing life failure mechanisms and modeling, the problem is still far from being solved. In this paper, we introduce a time-frequency feature extraction based method for estimating remaining useful life of bearings from vibration signals. The proposed approach extracts measures that quantify the complexity of time-frequency surfaces corresponding to vibration signals. The extracted features are then tracked through the life time of a bearing using curve fitting and Extended Kalman Filtering algorithms. The proposed methodology is tested on a publicly available bearing data set with known RULs.
{"title":"Time-frequency complexity based remaining useful life (RUL) estimation for bearing faults","authors":"Rodney K. Singleton, E. Strangas, Selin Aviyente","doi":"10.1109/DEMPED.2013.6645776","DOIUrl":"https://doi.org/10.1109/DEMPED.2013.6645776","url":null,"abstract":"Reliable operation of electrical machines depends on the timely detection and diagnosis of faults as well as on prognosis, i.e. estimating the remaining useful life (RUL) of the components. Bearings are the most common components in rotary machines and usually constitute a large portion of the failure cases in these machines. Although there has been a lot of work in the study of bearing life failure mechanisms and modeling, the problem is still far from being solved. In this paper, we introduce a time-frequency feature extraction based method for estimating remaining useful life of bearings from vibration signals. The proposed approach extracts measures that quantify the complexity of time-frequency surfaces corresponding to vibration signals. The extracted features are then tracked through the life time of a bearing using curve fitting and Extended Kalman Filtering algorithms. The proposed methodology is tested on a publicly available bearing data set with known RULs.","PeriodicalId":425644,"journal":{"name":"2013 9th IEEE International Symposium on Diagnostics for Electric Machines, Power Electronics and Drives (SDEMPED)","volume":"15 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2013-10-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123879819","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 : 2013-10-24DOI: 10.1109/DEMPED.2013.6645722
A. G. Garcia-Ramirez, R. Osornio-Ríos, A. García-Perez, R. Romero-Troncoso
Nowadays, different industrial processes use induction motors fed through variable speed drives (VSD). In order to improve these processes, the industry demands the use of smart sensors to detect the faults, reduce the cost of maintenance, and decrease power consumption. In this work, broken rotor bars, unbalance and misalignment are automatically detected in induction motors fed by a VSD using the three current phases online, with a smart sensor. The proposed smart sensor is implemented in a field programmable gate array offering a low computational load methodology, low- cost, and portable solution for fault detection in induction motors VSD-fed. Results show a high effectiveness detection of the treated faults.
{"title":"FPGA-based smart-sensor for fault detection in VSD-fed induction motors","authors":"A. G. Garcia-Ramirez, R. Osornio-Ríos, A. García-Perez, R. Romero-Troncoso","doi":"10.1109/DEMPED.2013.6645722","DOIUrl":"https://doi.org/10.1109/DEMPED.2013.6645722","url":null,"abstract":"Nowadays, different industrial processes use induction motors fed through variable speed drives (VSD). In order to improve these processes, the industry demands the use of smart sensors to detect the faults, reduce the cost of maintenance, and decrease power consumption. In this work, broken rotor bars, unbalance and misalignment are automatically detected in induction motors fed by a VSD using the three current phases online, with a smart sensor. The proposed smart sensor is implemented in a field programmable gate array offering a low computational load methodology, low- cost, and portable solution for fault detection in induction motors VSD-fed. Results show a high effectiveness detection of the treated faults.","PeriodicalId":425644,"journal":{"name":"2013 9th IEEE International Symposium on Diagnostics for Electric Machines, Power Electronics and Drives (SDEMPED)","volume":"03 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2013-10-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128774911","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 : 2013-10-24DOI: 10.1109/DEMPED.2013.6645770
A. Bacchus, M. Biet, L. Macaire, Y. Le Menach, A. Tounzi
The goal of this paper consists in applying pattern recognition methods to turbo-generators. Previous works have shown that a monitor, thanks to pattern recognition, is practical on asynchronous machines. This procedure has rarely taken advantage of these methods for turbogenerator. The statistical model has been obtained from harmonics extracted from flux probes and from stator current and voltage. For this purpose, the main way is to build a learning matrix to predict the functional state of a new measurement. Finally, three classifiers have been compared: k Nearest Neighbors, Linear Discriminant Analysis and Support Vector Machines. The best classification result is obtained by Linear Discriminant Analysis combined with Factorial Discriminant Analysis achieving a score of 84.6%.
{"title":"Comparison of supervised classification algorithms combined with feature extraction and selection: Application to a turbo-generator rotor fault detection","authors":"A. Bacchus, M. Biet, L. Macaire, Y. Le Menach, A. Tounzi","doi":"10.1109/DEMPED.2013.6645770","DOIUrl":"https://doi.org/10.1109/DEMPED.2013.6645770","url":null,"abstract":"The goal of this paper consists in applying pattern recognition methods to turbo-generators. Previous works have shown that a monitor, thanks to pattern recognition, is practical on asynchronous machines. This procedure has rarely taken advantage of these methods for turbogenerator. The statistical model has been obtained from harmonics extracted from flux probes and from stator current and voltage. For this purpose, the main way is to build a learning matrix to predict the functional state of a new measurement. Finally, three classifiers have been compared: k Nearest Neighbors, Linear Discriminant Analysis and Support Vector Machines. The best classification result is obtained by Linear Discriminant Analysis combined with Factorial Discriminant Analysis achieving a score of 84.6%.","PeriodicalId":425644,"journal":{"name":"2013 9th IEEE International Symposium on Diagnostics for Electric Machines, Power Electronics and Drives (SDEMPED)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2013-10-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123311909","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 : 2013-10-24DOI: 10.1109/DEMPED.2013.6645762
L. Zarri, M. Mengoni, A. Tani, Y. Gritli, G. Serra, F. Filippetti, D. Casadei
High-resistance connections in electrical machines cause unbalances in the stator resistances, reduce the efficiency and increase the fire hazard. In this paper the problem of detection of high-resistance connections is investigated for multiphase induction machines with an odd number of phases. The main contribution of the paper is a control scheme that can determine the stator resistance unbalance of all phases, in transient and in steady-state operating conditions. The theoretical analysis and the feasibility of the control scheme are confirmed by experimental tests.
{"title":"Full detection of high-resistance connections in multiphase induction machines","authors":"L. Zarri, M. Mengoni, A. Tani, Y. Gritli, G. Serra, F. Filippetti, D. Casadei","doi":"10.1109/DEMPED.2013.6645762","DOIUrl":"https://doi.org/10.1109/DEMPED.2013.6645762","url":null,"abstract":"High-resistance connections in electrical machines cause unbalances in the stator resistances, reduce the efficiency and increase the fire hazard. In this paper the problem of detection of high-resistance connections is investigated for multiphase induction machines with an odd number of phases. The main contribution of the paper is a control scheme that can determine the stator resistance unbalance of all phases, in transient and in steady-state operating conditions. The theoretical analysis and the feasibility of the control scheme are confirmed by experimental tests.","PeriodicalId":425644,"journal":{"name":"2013 9th IEEE International Symposium on Diagnostics for Electric Machines, Power Electronics and Drives (SDEMPED)","volume":"25 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2013-10-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130653627","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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