Pub Date : 2018-09-01DOI: 10.1109/CMD.2018.8535765
M. Anis
Estimating the Remaining Useful Life (RUL) of critical assets is not only a basic requirement of condition-based maintenance, but is also central to system prognostics for cost efficiency. A well professed definition of prognostics in the existing literature is the ability to use automated methods to assess system condition, estimate functional parameters and forecast degradation. Rotating shafts are a critical component to most modern day machinery and are at a constant risk of failure given the harsh working environment they are subjected to. The main aim of this paper is to propose a data-driven prognostic approach combining a machine learning method like Principal Component Analysis (PCA) with an exponential degradation model to accurately predict the RUL of a rotating shaft. For this purpose, vibration data collected off a faulty shaft over many days is analyzed in both time and frequency domains to extract descriptive fault features. Following feature post-processing for noise reduction and data training, it is observed that Kurtosis ranks the highest in terms of feature importance by quantifying its merit amongst all other features based on the metrics of monotonicity and trendability. Following feature normalization, a PCA model is employed for dimensionality reduction and feature fusion to improve the accuracy of the prognosis system. As a good indicator of deteriorating health, the PCA-based fused health indicator is combined with the previous top feature, Kurtosis, to be used as a mathematical input for a physical-behavior degradation model. Unlike most practical cases, the selection of threshold for a degradation slope in the proposed model is independent of historical data and is capable of evaluating the significance of slope by relying on observed data instead. Results indicate that parameter distribution is updated on a real-time basis by selecting an arbitrary slope parameter every time a significant variance in health is detected. The final output includes probability density function (PDF) of RUL, Estimated & True RUL, confidence intervals and prognostic performance analysis plots indicating better performance of the proposed degradation model in predicting shaft failure.
{"title":"Towards Remaining Useful Life Prediction in Rotating Machine Fault Prognosis: An Exponential Degradation Model","authors":"M. Anis","doi":"10.1109/CMD.2018.8535765","DOIUrl":"https://doi.org/10.1109/CMD.2018.8535765","url":null,"abstract":"Estimating the Remaining Useful Life (RUL) of critical assets is not only a basic requirement of condition-based maintenance, but is also central to system prognostics for cost efficiency. A well professed definition of prognostics in the existing literature is the ability to use automated methods to assess system condition, estimate functional parameters and forecast degradation. Rotating shafts are a critical component to most modern day machinery and are at a constant risk of failure given the harsh working environment they are subjected to. The main aim of this paper is to propose a data-driven prognostic approach combining a machine learning method like Principal Component Analysis (PCA) with an exponential degradation model to accurately predict the RUL of a rotating shaft. For this purpose, vibration data collected off a faulty shaft over many days is analyzed in both time and frequency domains to extract descriptive fault features. Following feature post-processing for noise reduction and data training, it is observed that Kurtosis ranks the highest in terms of feature importance by quantifying its merit amongst all other features based on the metrics of monotonicity and trendability. Following feature normalization, a PCA model is employed for dimensionality reduction and feature fusion to improve the accuracy of the prognosis system. As a good indicator of deteriorating health, the PCA-based fused health indicator is combined with the previous top feature, Kurtosis, to be used as a mathematical input for a physical-behavior degradation model. Unlike most practical cases, the selection of threshold for a degradation slope in the proposed model is independent of historical data and is capable of evaluating the significance of slope by relying on observed data instead. Results indicate that parameter distribution is updated on a real-time basis by selecting an arbitrary slope parameter every time a significant variance in health is detected. The final output includes probability density function (PDF) of RUL, Estimated & True RUL, confidence intervals and prognostic performance analysis plots indicating better performance of the proposed degradation model in predicting shaft failure.","PeriodicalId":6529,"journal":{"name":"2018 Condition Monitoring and Diagnosis (CMD)","volume":"13 1","pages":"1-6"},"PeriodicalIF":0.0,"publicationDate":"2018-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"87757316","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 : 2018-09-01DOI: 10.1109/CMD.2018.8535726
Sai Srinivas Manohar, A. Subramaniam, M. Bagheri, S. Nadarajan, A. Gupta, S. K. Panda
The role of transformer in electricity network reliability and safe operation is quite crucial. Therefore, their monitoring, maintenance and management is vitally important for utility operators. Simpler, non-invasive and online condition monitoring method which is sensitive to incipient faults is in demand for transformer diagnosis. Although vibration monitoring is considered as a valuable technique in industry for rotating machines, it is least explored for static electrical equipment such as transformer. In this study, core and winding vibrations in a dry type transformer is monitored using an accelerometer to diagnose the winding electrical and mechanical faults. Winding and core vibrations for various degrees of inter-turn fault, axial movement and disc displacement under different load conditions are studied.
{"title":"Transformer Winding Fault Diagnosis by Vibration Monitoring","authors":"Sai Srinivas Manohar, A. Subramaniam, M. Bagheri, S. Nadarajan, A. Gupta, S. K. Panda","doi":"10.1109/CMD.2018.8535726","DOIUrl":"https://doi.org/10.1109/CMD.2018.8535726","url":null,"abstract":"The role of transformer in electricity network reliability and safe operation is quite crucial. Therefore, their monitoring, maintenance and management is vitally important for utility operators. Simpler, non-invasive and online condition monitoring method which is sensitive to incipient faults is in demand for transformer diagnosis. Although vibration monitoring is considered as a valuable technique in industry for rotating machines, it is least explored for static electrical equipment such as transformer. In this study, core and winding vibrations in a dry type transformer is monitored using an accelerometer to diagnose the winding electrical and mechanical faults. Winding and core vibrations for various degrees of inter-turn fault, axial movement and disc displacement under different load conditions are studied.","PeriodicalId":6529,"journal":{"name":"2018 Condition Monitoring and Diagnosis (CMD)","volume":"56 1","pages":"1-6"},"PeriodicalIF":0.0,"publicationDate":"2018-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"89055002","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 : 2018-09-01DOI: 10.1109/CMD.2018.8535856
I. Sari, Suwarno, T. Kinkeldey, P. Werle
Transformers are critical components of the power grid. The energy transfer is highly dependent on the performance of the transformer. As the transformer operates, the paper insulation undergoes an aging process as a result of thermal, electrical, and mechanical stresses. The chemical and physical characteristics of the paper insulation change gradually. Paper insulation which mostly consists of cellulose degrades and the Degree of Polymerization decreases due to the decomposition of the inter-fiber bondings. This causes a reduced mechanical strength and will lead to tears, defibrillations and loss of stability. In this contribution, the mechanical behavior of paper insulation impregnated with uninhibited oil, and 2 different inhibited oils investigated. The observation focus is on the mechanical characteristics of paper insulation by considering and the degree of polymerization and the Tensile Strength.
{"title":"Effect of Thermal Aging on the mechanical Characteristic of Insulating Paper Impregnated with Different Insulating Oils","authors":"I. Sari, Suwarno, T. Kinkeldey, P. Werle","doi":"10.1109/CMD.2018.8535856","DOIUrl":"https://doi.org/10.1109/CMD.2018.8535856","url":null,"abstract":"Transformers are critical components of the power grid. The energy transfer is highly dependent on the performance of the transformer. As the transformer operates, the paper insulation undergoes an aging process as a result of thermal, electrical, and mechanical stresses. The chemical and physical characteristics of the paper insulation change gradually. Paper insulation which mostly consists of cellulose degrades and the Degree of Polymerization decreases due to the decomposition of the inter-fiber bondings. This causes a reduced mechanical strength and will lead to tears, defibrillations and loss of stability. In this contribution, the mechanical behavior of paper insulation impregnated with uninhibited oil, and 2 different inhibited oils investigated. The observation focus is on the mechanical characteristics of paper insulation by considering and the degree of polymerization and the Tensile Strength.","PeriodicalId":6529,"journal":{"name":"2018 Condition Monitoring and Diagnosis (CMD)","volume":"68 1","pages":"1-4"},"PeriodicalIF":0.0,"publicationDate":"2018-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"81191402","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 : 2018-09-01DOI: 10.1109/CMD.2018.8535894
Hanqing Liang, G. Sheng, X. Mao, Xiuchen Jiang, Yadong Liu
Obtaining key transient information at fault points accurately is of practical significance in failure-cause identification and rebuilding plan formulation for overhead transmission line (OTL). The paper proposed a method for excavating key transient information during unsymmetrical earth fault. Based on the inversion theory, the required transient current traveling wave at fault points could be obtained. Combining with fault type, a mathematical model of current amplitude, fault angle and fault resistance was constructed. Furthermore, improved firefly algorithm (IFFA) was adopted to solve model parameter. Simulation results demonstrated that the proposed method contributed to accurately reproduce traveling wave at the fault points and excavate the key transient fault information.
{"title":"Methodology for Obtaining Key Transient Information of Unsymmetrical Earth Fault in Overhead Transmission Line","authors":"Hanqing Liang, G. Sheng, X. Mao, Xiuchen Jiang, Yadong Liu","doi":"10.1109/CMD.2018.8535894","DOIUrl":"https://doi.org/10.1109/CMD.2018.8535894","url":null,"abstract":"Obtaining key transient information at fault points accurately is of practical significance in failure-cause identification and rebuilding plan formulation for overhead transmission line (OTL). The paper proposed a method for excavating key transient information during unsymmetrical earth fault. Based on the inversion theory, the required transient current traveling wave at fault points could be obtained. Combining with fault type, a mathematical model of current amplitude, fault angle and fault resistance was constructed. Furthermore, improved firefly algorithm (IFFA) was adopted to solve model parameter. Simulation results demonstrated that the proposed method contributed to accurately reproduce traveling wave at the fault points and excavate the key transient fault information.","PeriodicalId":6529,"journal":{"name":"2018 Condition Monitoring and Diagnosis (CMD)","volume":"1 1","pages":"1-6"},"PeriodicalIF":0.0,"publicationDate":"2018-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"75637583","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 : 2018-09-01DOI: 10.1109/CMD.2018.8535662
Haohui Su, Qi Wang, Yanzhou Chen, Yiming Wang, B. Qi, Peng Zhang, Chengrong Li
The HVDC has entered a period of rapid development in China. As the core of HVDC transmission, the converter station has the characteristics of many types of equipment, various equipment, various auxiliary systems and large signal levels, and it has a high requirement for real-time monitoring signals. To realize the remote monitoring and control of the converter station, it is urgent to solve the problem of stratification and classification of the remote transmission signal. Therefore, this paper mainly focuses on the remote transmission signals of large converter stations, and conducts stratification and classification strategies from the three business perspectives of converter station remote monitoring, DC system status identification, and event intelligent diagnosis and processing. First of all, this paper investigates the research status of stratification and classification of converter station signals, and combs and collects the remote transmission signals. Then, combined with the domestic high-voltage DC protection system and the actual operation data of the telecontrol system, the stratification and classification strategies of the far-transmission signal of the converter station are studied from three different angles. Finally, a differentiated signal stratification and classification method for different services based on grey correlation analysis and Apriori correlation analysis was proposed. The actual case verification can get the result. The signal stratification and classification method proposed in this paper can accurately filter the signals which used to evaluate the status of the converter station. The sets of signal can be used to generate a signal importance list for different devices.
{"title":"Hierarchical Layered Method of Converter Station Based on Principal Component Analysis and Association Analysis","authors":"Haohui Su, Qi Wang, Yanzhou Chen, Yiming Wang, B. Qi, Peng Zhang, Chengrong Li","doi":"10.1109/CMD.2018.8535662","DOIUrl":"https://doi.org/10.1109/CMD.2018.8535662","url":null,"abstract":"The HVDC has entered a period of rapid development in China. As the core of HVDC transmission, the converter station has the characteristics of many types of equipment, various equipment, various auxiliary systems and large signal levels, and it has a high requirement for real-time monitoring signals. To realize the remote monitoring and control of the converter station, it is urgent to solve the problem of stratification and classification of the remote transmission signal. Therefore, this paper mainly focuses on the remote transmission signals of large converter stations, and conducts stratification and classification strategies from the three business perspectives of converter station remote monitoring, DC system status identification, and event intelligent diagnosis and processing. First of all, this paper investigates the research status of stratification and classification of converter station signals, and combs and collects the remote transmission signals. Then, combined with the domestic high-voltage DC protection system and the actual operation data of the telecontrol system, the stratification and classification strategies of the far-transmission signal of the converter station are studied from three different angles. Finally, a differentiated signal stratification and classification method for different services based on grey correlation analysis and Apriori correlation analysis was proposed. The actual case verification can get the result. The signal stratification and classification method proposed in this paper can accurately filter the signals which used to evaluate the status of the converter station. The sets of signal can be used to generate a signal importance list for different devices.","PeriodicalId":6529,"journal":{"name":"2018 Condition Monitoring and Diagnosis (CMD)","volume":"11 1","pages":"1-7"},"PeriodicalIF":0.0,"publicationDate":"2018-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"81913772","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 : 2018-09-01DOI: 10.1109/CMD.2018.8535621
Songlin Jiang, Qinxue Yu, Chen Zhang, Huaqiang Li, L. Zhong, Yu Xu, X. Hu, Y. Shuai
In this paper, negative polarity lighting impulse properties of three kinds of transformer oils in different gaps were investigated. The tested oils included 25# (naphthenic mineral oil), RAPO (natural ester, rape-seed oil) and FR3 (natural ester, soybean oil). Each of oils was tested with and without pressboard under 1.2/50µs standard negative lighting impulse with point-plane electrode system in several gaps. Breakdown voltage (in oil without pressboard) and flashover voltage (in oil with pressboard) were recorded and analyzed. It has been found that the above natural esters represented certain degree of lower breakdown and flashover properties compared to mineral oil; moreover, in small oil gaps, the breakdown voltage was higher than flashover voltage, while in large gaps the opposite, and this trend was increasingly obvious with gap distance increasing. Furthermore, this paper found that other factors had a certain influence on the negative lighting impulse properties, which will be investigated in future.
{"title":"Lighting Impulse Properties in Large Oil Gaps with and without Pressboard Interface","authors":"Songlin Jiang, Qinxue Yu, Chen Zhang, Huaqiang Li, L. Zhong, Yu Xu, X. Hu, Y. Shuai","doi":"10.1109/CMD.2018.8535621","DOIUrl":"https://doi.org/10.1109/CMD.2018.8535621","url":null,"abstract":"In this paper, negative polarity lighting impulse properties of three kinds of transformer oils in different gaps were investigated. The tested oils included 25# (naphthenic mineral oil), RAPO (natural ester, rape-seed oil) and FR3 (natural ester, soybean oil). Each of oils was tested with and without pressboard under 1.2/50µs standard negative lighting impulse with point-plane electrode system in several gaps. Breakdown voltage (in oil without pressboard) and flashover voltage (in oil with pressboard) were recorded and analyzed. It has been found that the above natural esters represented certain degree of lower breakdown and flashover properties compared to mineral oil; moreover, in small oil gaps, the breakdown voltage was higher than flashover voltage, while in large gaps the opposite, and this trend was increasingly obvious with gap distance increasing. Furthermore, this paper found that other factors had a certain influence on the negative lighting impulse properties, which will be investigated in future.","PeriodicalId":6529,"journal":{"name":"2018 Condition Monitoring and Diagnosis (CMD)","volume":"28 1","pages":"1-5"},"PeriodicalIF":0.0,"publicationDate":"2018-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"75541790","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 : 2018-09-01DOI: 10.1109/CMD.2018.8535885
Xinbo Huang, Zhiwen Li, Yongcan Zhu
The power equipment is an important component of the power system, which will seriously threat the stability of power system when the heat fault occurs during its running. An integrated system has been designed in this paper directed against the characteristics of thermal fault, which can implement the functionality of temperature acquisition, realtime display and fault warning. The real-time temperature can be stably collected via wireless transmission, low-power technology and so on. The dynamic threshold algorithm based on beta distribution is used to eliminate the singularity data that potential introduced in the process of data transmission or acquisition. The development trend of the equipment temperature can be predicted by means of the temperature prediction model established through the process neural network. The experimental results show that the system can effectively measure and display the temperature of power equipment and predict the development of temperature trend, which has higher precision.
{"title":"The System of Temperature Rise Monitoring and Temperature Prediction for Power Equipment","authors":"Xinbo Huang, Zhiwen Li, Yongcan Zhu","doi":"10.1109/CMD.2018.8535885","DOIUrl":"https://doi.org/10.1109/CMD.2018.8535885","url":null,"abstract":"The power equipment is an important component of the power system, which will seriously threat the stability of power system when the heat fault occurs during its running. An integrated system has been designed in this paper directed against the characteristics of thermal fault, which can implement the functionality of temperature acquisition, realtime display and fault warning. The real-time temperature can be stably collected via wireless transmission, low-power technology and so on. The dynamic threshold algorithm based on beta distribution is used to eliminate the singularity data that potential introduced in the process of data transmission or acquisition. The development trend of the equipment temperature can be predicted by means of the temperature prediction model established through the process neural network. The experimental results show that the system can effectively measure and display the temperature of power equipment and predict the development of temperature trend, which has higher precision.","PeriodicalId":6529,"journal":{"name":"2018 Condition Monitoring and Diagnosis (CMD)","volume":"6 1","pages":"1-5"},"PeriodicalIF":0.0,"publicationDate":"2018-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"75605894","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}
In order to deal with the limitations on the feature extraction of strong random signals in DC XLPE cables, this paper proposes a self-adaptive pattern recognition method based on convolutional neural network (CNN). Convolutional Architecture for Fast Feature Embedding (Caffe) has great performance on image recognition using CNN. Four typical insulation defects are designed and PD signals are collected for pattern recognition. Four different Caffe frameworks are constructed to analyze the impact of the network structures and solver parameters on training effect. Compared with Quick-CIFAR-IO and original Alexnet network, the modified Alexnet network proposed by this paper has great adaptability to pattern recognition of partial discharges in DC XLPE cables.
{"title":"Partial discharge pattern recognition of DC XLPE cables based on convolutional neural network","authors":"Yufeng Zhu, Yongpeng Xu, Jingde Chen, Fan Rusen, Sheng Gehao, Xiuchen Jiang","doi":"10.1109/CMD.2018.8535793","DOIUrl":"https://doi.org/10.1109/CMD.2018.8535793","url":null,"abstract":"In order to deal with the limitations on the feature extraction of strong random signals in DC XLPE cables, this paper proposes a self-adaptive pattern recognition method based on convolutional neural network (CNN). Convolutional Architecture for Fast Feature Embedding (Caffe) has great performance on image recognition using CNN. Four typical insulation defects are designed and PD signals are collected for pattern recognition. Four different Caffe frameworks are constructed to analyze the impact of the network structures and solver parameters on training effect. Compared with Quick-CIFAR-IO and original Alexnet network, the modified Alexnet network proposed by this paper has great adaptability to pattern recognition of partial discharges in DC XLPE cables.","PeriodicalId":6529,"journal":{"name":"2018 Condition Monitoring and Diagnosis (CMD)","volume":"23 1","pages":"1-6"},"PeriodicalIF":0.0,"publicationDate":"2018-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"77497502","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 : 2018-09-01DOI: 10.1109/CMD.2018.8535782
Wen Yan, Meng Hailei, Fang Mu, Dai Jindun, L. Yadong
To improve the reliability and sensitivity of fault location in distribution networks, a fault location method based on full waveform information is presented. In this method, the acting features of arc suppression coil and the influence of arc suppression coils parameters on zero sequence voltage and zero sequence current are considered. For grounding faults, the maximum point of the cross-correlation function of the zero sequence voltage of buses and the zero sequence current on each outgoing line is used to select the faulty line, and then the standardized deviation degree of the zero sequence current waveforms when the fault occurs and the corresponding arc suppression coil operates is used to localize the fault. For interphase faults, the three-phase current information before the relay protection device acts is measured to select the faulty line, and then by using standardized deviation degree of the faulty phase current waveforms, the fault is localized. Through the simulations on PSCAD/EMTDC software, the feasibility and reliability of the proposed method are verified.
{"title":"Fault Location Method Based on Full Waveform Information for Distribution Networks","authors":"Wen Yan, Meng Hailei, Fang Mu, Dai Jindun, L. Yadong","doi":"10.1109/CMD.2018.8535782","DOIUrl":"https://doi.org/10.1109/CMD.2018.8535782","url":null,"abstract":"To improve the reliability and sensitivity of fault location in distribution networks, a fault location method based on full waveform information is presented. In this method, the acting features of arc suppression coil and the influence of arc suppression coils parameters on zero sequence voltage and zero sequence current are considered. For grounding faults, the maximum point of the cross-correlation function of the zero sequence voltage of buses and the zero sequence current on each outgoing line is used to select the faulty line, and then the standardized deviation degree of the zero sequence current waveforms when the fault occurs and the corresponding arc suppression coil operates is used to localize the fault. For interphase faults, the three-phase current information before the relay protection device acts is measured to select the faulty line, and then by using standardized deviation degree of the faulty phase current waveforms, the fault is localized. Through the simulations on PSCAD/EMTDC software, the feasibility and reliability of the proposed method are verified.","PeriodicalId":6529,"journal":{"name":"2018 Condition Monitoring and Diagnosis (CMD)","volume":"14 1","pages":"1-5"},"PeriodicalIF":0.0,"publicationDate":"2018-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"73909966","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 : 2018-09-01DOI: 10.1109/CMD.2018.8535596
W. B. Hassen, M. Kafal, E. Cabanillas, J. Benoit
Several approaches have been proposed and applied for reconstructing the topology of an unknown network. Although, promising results have been obtained, offline passive testing was only accessible. On the other hand, a wide range of wiring networks embedded in critical systems as power grids and power-plants can not be easily shutdown for testing purposes. Accordingly, we will propose in this paper an approach for diagnosing obscured networks in an on-line live mode, thanks to the Orthogonal Multi-tone Time Domain Reflectometry (OMTDR). Optimization techniques namely the genetic algorithm will be integrated with the OMTDR method to enable revealing the topology of the black-boxed tested network. Practical real-life experimental setups are dedicated to validate the proposed approach.
{"title":"Power Cable Network Topology Reconstruction Using Multi-carrier Reflectometry for Fault Detection and Location in Live Smart Grids","authors":"W. B. Hassen, M. Kafal, E. Cabanillas, J. Benoit","doi":"10.1109/CMD.2018.8535596","DOIUrl":"https://doi.org/10.1109/CMD.2018.8535596","url":null,"abstract":"Several approaches have been proposed and applied for reconstructing the topology of an unknown network. Although, promising results have been obtained, offline passive testing was only accessible. On the other hand, a wide range of wiring networks embedded in critical systems as power grids and power-plants can not be easily shutdown for testing purposes. Accordingly, we will propose in this paper an approach for diagnosing obscured networks in an on-line live mode, thanks to the Orthogonal Multi-tone Time Domain Reflectometry (OMTDR). Optimization techniques namely the genetic algorithm will be integrated with the OMTDR method to enable revealing the topology of the black-boxed tested network. Practical real-life experimental setups are dedicated to validate the proposed approach.","PeriodicalId":6529,"journal":{"name":"2018 Condition Monitoring and Diagnosis (CMD)","volume":"46 1","pages":"1-5"},"PeriodicalIF":0.0,"publicationDate":"2018-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"74446462","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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