Internal short circuit(ISCr) is one of the major obstacles to the improvement of the battery safety. The ISCr may lead to the battery thermal runaway and is hard to be detected in the early stage. In this work, a new ...Internal short circuit(ISCr) is one of the major obstacles to the improvement of the battery safety. The ISCr may lead to the battery thermal runaway and is hard to be detected in the early stage. In this work, a new ISCr detection method based on the symmetrical loop circuit topology(SLCT) is introduced. The SLCT ensures that every battery has the same priority in the circuit and every battery will contribute the same amount of short-circuit current to the ISCr once the ISCr happens. The ISCr battery could be identified by the combination of the ratio of the short-circuit currents and the sign of the short-circuit currents. The recursive least square method is adopted for the real-time application and the optimized ammeters allocation is derived from the mathematic deduction. The battery pack based on the individual DP(dual polarization) battery model is established to verify the ISCr detection method. The 1–1000 Ω s ISCr(the early stage ISCr) can be effectively detected within 1–125 s. The SLCT provides the possibility of new battery pack designs and new battery management methods. The proposed ISCr detection method shows excellent effectiveness and efficiency on the identification of the ISCr battery in the early stage.展开更多
Based on principal component analysis, this paper presents an application of faulty sensor detection and reconstruction in a batch process, polyvinylchloride (PVC) making process. To deal with inconsistency in process...Based on principal component analysis, this paper presents an application of faulty sensor detection and reconstruction in a batch process, polyvinylchloride (PVC) making process. To deal with inconsistency in process data, it is proposed to use the dynamic time warping technique to make the historical data synchronized first,then build a consistent multi-way principal component analysis model. Fault detection is carried out based on squared prediction error statistical control plot. By defining principal component subspace, residual subspace and sensor validity index, faulty sensor can be reconstructed and identified along the fault direction. Finally, application results are illustrated in detail by use of the real data of an industrial PVC making process.展开更多
Accurate fault area localization is a challenging problem in resonant grounding systems(RGSs).Accordingly,this paper proposes a novel two-stage localization method for single-phase earth faults in RGSs.Firstly,a fault...Accurate fault area localization is a challenging problem in resonant grounding systems(RGSs).Accordingly,this paper proposes a novel two-stage localization method for single-phase earth faults in RGSs.Firstly,a faulty feeder identification algorithm based on a Bayesian classifier is proposed.Three characteristic parameters of the RGS(the energy ratio,impedance factor,and energy spectrum entropy)are calculated based on the zero-sequence current(ZSC)of each feeder using wavelet packet transformations.Then,the values of three parameters are sent to a pre-trained Bayesian classifier to recognize the exact fault mode.With this result,the faulty feeder can be finally identified.To find the exact fault area on the faulty feeder,a localization method based on the similarity comparison of dominant frequency-band waveforms is proposed in an RGS equipped with feeder terminal units(FTUs).The FTUs can provide the information on the ZSC at their locations.Through wavelet-packet transformation,ZSC dominant frequency-band waveforms can be obtained at all FTU points.Similarities of the waveforms of characteristics at all FTU points are calculated and compared.The neighboring FTU points with the maximum diversity are the faulty sections finally determined.The proposed method exhibits higher accuracy in both faulty feeder identification and fault area localization compared to the previous methods.Finally,the effectiveness of the proposed method is validated by comparing simulation and experimental results.展开更多
Identification of faulty feeders in resonant grounding distribution networks remains a significant challenge dueto the weak fault current and complicated working conditions.In this paper, we present a deep learning-ba...Identification of faulty feeders in resonant grounding distribution networks remains a significant challenge dueto the weak fault current and complicated working conditions.In this paper, we present a deep learning-based multi-labelclassification framework to reliably distinguish the faulty feeder.Three different neural networks (NNs) including the multilayerperceptron, one-dimensional convolutional neural network (1DCNN), and 2D CNN are built. However, the labeled data maybe difficult to obtain in the actual environment. We use thesimplified simulation model based on a full-scale test field (FSTF)to obtain sufficient labeled source data. Being different frommost learning-based methods, assuming that the distribution ofsource domain and target domain is identical, we propose asamples-based transfer learning method to improve the domainadaptation by using samples in the source domain with properweights. The TrAdaBoost algorithm is adopted to update theweights of each sample. The recorded data obtained in the FSTFare utilized to test the domain adaptability. According to ourvalidation and testing, the validation accuracies are high whenthere is sufficient labeled data for training the proposed NNs.The proposed 2D CNN has the best domain adaptability. TheTrAdaBoost algorithm can help the NNs to train an efficientclassifier that has better domain adaptation. It has been thereforeconcluded that the proposed method, especially the 2D CNN, issuitable for actual distribution networks.展开更多
Sensor networks are deployed in many application areas nowadays ranging from environment monitoring, industrial monitoring, and agriculture monitoring to military battlefield sensing. The accuracy of sensor readings i...Sensor networks are deployed in many application areas nowadays ranging from environment monitoring, industrial monitoring, and agriculture monitoring to military battlefield sensing. The accuracy of sensor readings is without a doubt one of the most important measures to evaluate the quality of a sensor and its network. Therefore, this work is motivated to propose approaches that can detect and repair erroneous （i.e., dirty） data caused by inevitable system problems involving various hardware and software components of sensor networks. As information about a single event of interest in a sensor network is usually reflected in multiple measurement points, the inconsistency among multiple sensor measurements serves as an indicator for data quality problem. The focus of this paper is thus to study methods that can effectively detect and identify erroneous data among inconsistent observations based on the inherent structure of various sensor measurement series from a group of sensors. Particularly, we present three models to characterize the inherent data structures among sensor measurement traces and then apply these models individually to guide the error detection of a sensor network. First, we propose a multivariate Gaussian model which explores the correlated data changes of a group of sensors. Second, we present a Principal Component Analysis （PCA） model which captures the sparse geometric relationship among sensors in a network. The PCA model is motivated by the fact that not all sensor networks have clustered sensor deployment and clear data correlation structure. Further, if the sensor data show non-linear characteristic, a traditional PCA model can not capture the data attributes properly. Therefore, we propose a third model which utilizes kernel functions to map the original data into a high dimensional feature space and then apply PCA model on the mapped linearized data. All these three models serve the purpose of capturing the underlying phenomenon of a sensor network from its global view, and th展开更多
With the increasing number of switches in Software-Defined Network-ing(SDN),there are more and more faults rising in the data plane.However,due to the existence of link redundancy and multi-path forwarding mechanisms,t...With the increasing number of switches in Software-Defined Network-ing(SDN),there are more and more faults rising in the data plane.However,due to the existence of link redundancy and multi-path forwarding mechanisms,these problems cannot be detected in time.The current faulty path detection mechan-isms have problems such as the large scale of detection and low efficiency,which is difficult to meet the requirements of efficient faulty path detection in large-scale SDN.Concerning this issue,we propose an efficient network path fault testing model ProbD based on probability detection.This model achieves a high prob-ability of detecting arbitrary path fault in the form of small-scale random sam-pling.Under a certain path fault rate,ProbD obtains the curve of sample size and probability of detecting arbitrary path fault by randomly sampling network paths several times.After a small number of experiments,the ProbD model can cor-rectly estimate the path fault rate of the network and calculate the total number of paths that need to be detected according to the different probability of detecting arbitrary path fault and the path fault rate of the network.Thefinal experimental results show that,compared with the full path coverage test,the ProbD model based on probability detection can achieve efficient network testing with less overhead.Besides,the larger the network scale is,the more overhead will be saved.展开更多
This paper describes the method of built-in self-repairing of RAM on board, designs hardware circuit, and logic for the RAM's faults self-repairing system based on FPGA. The key technology is that it utilizes FPGA...This paper describes the method of built-in self-repairing of RAM on board, designs hardware circuit, and logic for the RAM's faults self-repairing system based on FPGA. The key technology is that it utilizes FPGA to test RAM according to some algorithm to find out failure memory units and replace the faulty units with FPGA. Then it can build a memory that has no fault concern to external controller, and realizes the logic binding between external controller and RAM. Micro Controller Unit (MCU) can operate external RAM correctly even if RAM has some fault address units. Conventional MCS-51 is used to simulate the operation of MCU operating external memory. Simulation shows FPGA can complete the faulty address units' mapping and MCU can normally read and write external RAM. This design realizes the RAM's built-in self-repairing on board.展开更多
This paper presents a new robust adaptive synchronization method for a class of uncertain dynamical complex networks with network failures and coupling time-varying delays. Adaptive schemes are proposed to adjust cont...This paper presents a new robust adaptive synchronization method for a class of uncertain dynamical complex networks with network failures and coupling time-varying delays. Adaptive schemes are proposed to adjust controller parameters for the faulty network compensations, as well as to estimate the upper and lower bounds of delayed state errors and perturbations to compensate the effects of delay and perturbation on-line without assuming symmetry or irreducibility of networks. It is shown that, through Lyapunov stability theory, distributed adaptive controllers con- structed by the adaptive schemes are successful in ensuring the achievement of asymptotic synchronization of networks in the present of faulty and delayed networks, and perturbation inputs. A Chua＇s circuit network example is finally given to show the effectiveness of the proposed synchronization criteria.展开更多
In the first part of this- paper, three generalizations of arrangement graph A.,k of [1], namely Bn,k, Cn,k and Dn,k , are introduced. We prove that all the three classes of graphs are vertex symmetric, two of them ar...In the first part of this- paper, three generalizations of arrangement graph A.,k of [1], namely Bn,k, Cn,k and Dn,k , are introduced. We prove that all the three classes of graphs are vertex symmetric, two of them are edge symmetric. They have great faulty tolerance and high connectivity. We give the diameters of B..k and Cn,k, the Hamiltonian cycle of Cn,k and Hamiltonian path of B.,k. We list several open problems, one of them related to the complexity of sorting algorithm on the arrangement graphs. All these graphs can be thought as generalizations of star graph but are more flexible so that they can be considered as new interconnection network topologies. In the second part of this paper, we provide other four classes of combinatorial graphes, Chn , Cyn, Zhn and Zyn. Many good properties of them, such as high node--connectivity, node symmetry, edge symmetry, diameter, ets., are shown in this paper.展开更多
Interfacing DC sources to load/power grid requires DC converters that produce minimum level of current ripples. This is to limit the losses and hence increase the life span of these sources. This article proposes a si...Interfacing DC sources to load/power grid requires DC converters that produce minimum level of current ripples. This is to limit the losses and hence increase the life span of these sources. This article proposes a simple inter-leaved boost converter that interfaces PhotoVoltaic (PV) module into a common DC-link. The article also addresses the faulty mode operation of the proposed circuit while advising the appropriate remedy actions. A MATLAB and Simulink dynamic platform are used to simulate the transient performance of the proposed converter. The results revealed the effectiveness and the viability of the proposed converter in reducing the ripples in the PV current without employing bulky input inductors or increasing the switching frequency.展开更多
We propose a fault-tolerant tree-based multicast algorithm for 2-dimensional (2-D) meshes based on the concept of the extended safety level which is a vector associated with each node to capture fault information in t...We propose a fault-tolerant tree-based multicast algorithm for 2-dimensional (2-D) meshes based on the concept of the extended safety level which is a vector associated with each node to capture fault information in the neighborhood. In this approach each destination is reached through a minimum number of hops. In order to minimize the total number of traffic steps, three heuristic strategies are proposed. This approach can be easily implemented by pipelined circuit switching (PCS). A simulation study is conducted to measure the total number of traffic steps under different strategies. Our approach is the first attempt to address the fault- tolerant tree-based multicast problem in 2-D meshes based on limited global information with a simple model and succinct information.展开更多
Fault management study in smart grid systems (SGSs) is important to ensure the stability of the system. Also, it is important to know the major types of power failures for the effective operation of the SGS. This pape...Fault management study in smart grid systems (SGSs) is important to ensure the stability of the system. Also, it is important to know the major types of power failures for the effective operation of the SGS. This paper reviews diverse types of faults that might appear in the SGS and gives a survey about the impact of renewable energy resources (RERs) on the behavior of the system. Moreover, this paper offers different fault detection and localization techniques that can be used for SGSs. Furthermore, a potential fault management case study is proposed in this paper. The SGS model in this paper is investigated using both of the Matlab/Simulink and the Real Time Digital Simulation (RTDS) to compute the fault management study. Simulation results show the fast response to a power failure in the system which improves the stability of the SGS.展开更多
基金supported by the National Natural Science Foundation of China (Grant No. U1564205)the Ministry of Science and Technology of China (Grant No. 2016YFE0102200)funded by China Scholarship Council
文摘Internal short circuit(ISCr) is one of the major obstacles to the improvement of the battery safety. The ISCr may lead to the battery thermal runaway and is hard to be detected in the early stage. In this work, a new ISCr detection method based on the symmetrical loop circuit topology(SLCT) is introduced. The SLCT ensures that every battery has the same priority in the circuit and every battery will contribute the same amount of short-circuit current to the ISCr once the ISCr happens. The ISCr battery could be identified by the combination of the ratio of the short-circuit currents and the sign of the short-circuit currents. The recursive least square method is adopted for the real-time application and the optimized ammeters allocation is derived from the mathematic deduction. The battery pack based on the individual DP(dual polarization) battery model is established to verify the ISCr detection method. The 1–1000 Ω s ISCr(the early stage ISCr) can be effectively detected within 1–125 s. The SLCT provides the possibility of new battery pack designs and new battery management methods. The proposed ISCr detection method shows excellent effectiveness and efficiency on the identification of the ISCr battery in the early stage.
基金Supported by the National Natural Science Foundation of China (No. 60025307, No. 60234010, No. 60028001), partially sup- ported by the National 863 Project (No. 2002AA412420),Rrsearch Fund for the Doctoral Program of Higer Education (No. 20020003063) and
文摘Based on principal component analysis, this paper presents an application of faulty sensor detection and reconstruction in a batch process, polyvinylchloride (PVC) making process. To deal with inconsistency in process data, it is proposed to use the dynamic time warping technique to make the historical data synchronized first,then build a consistent multi-way principal component analysis model. Fault detection is carried out based on squared prediction error statistical control plot. By defining principal component subspace, residual subspace and sensor validity index, faulty sensor can be reconstructed and identified along the fault direction. Finally, application results are illustrated in detail by use of the real data of an industrial PVC making process.
文摘Accurate fault area localization is a challenging problem in resonant grounding systems(RGSs).Accordingly,this paper proposes a novel two-stage localization method for single-phase earth faults in RGSs.Firstly,a faulty feeder identification algorithm based on a Bayesian classifier is proposed.Three characteristic parameters of the RGS(the energy ratio,impedance factor,and energy spectrum entropy)are calculated based on the zero-sequence current(ZSC)of each feeder using wavelet packet transformations.Then,the values of three parameters are sent to a pre-trained Bayesian classifier to recognize the exact fault mode.With this result,the faulty feeder can be finally identified.To find the exact fault area on the faulty feeder,a localization method based on the similarity comparison of dominant frequency-band waveforms is proposed in an RGS equipped with feeder terminal units(FTUs).The FTUs can provide the information on the ZSC at their locations.Through wavelet-packet transformation,ZSC dominant frequency-band waveforms can be obtained at all FTU points.Similarities of the waveforms of characteristics at all FTU points are calculated and compared.The neighboring FTU points with the maximum diversity are the faulty sections finally determined.The proposed method exhibits higher accuracy in both faulty feeder identification and fault area localization compared to the previous methods.Finally,the effectiveness of the proposed method is validated by comparing simulation and experimental results.
基金the Key Program of the Chinese Academy of Sciences under Grant QYZDJ-SSW-JSC025in part by the National Natural Science Foundation of China under Grant 51721005,and in part by the Chinese Scholarship Council(CSC).
文摘Identification of faulty feeders in resonant grounding distribution networks remains a significant challenge dueto the weak fault current and complicated working conditions.In this paper, we present a deep learning-based multi-labelclassification framework to reliably distinguish the faulty feeder.Three different neural networks (NNs) including the multilayerperceptron, one-dimensional convolutional neural network (1DCNN), and 2D CNN are built. However, the labeled data maybe difficult to obtain in the actual environment. We use thesimplified simulation model based on a full-scale test field (FSTF)to obtain sufficient labeled source data. Being different frommost learning-based methods, assuming that the distribution ofsource domain and target domain is identical, we propose asamples-based transfer learning method to improve the domainadaptation by using samples in the source domain with properweights. The TrAdaBoost algorithm is adopted to update theweights of each sample. The recorded data obtained in the FSTFare utilized to test the domain adaptability. According to ourvalidation and testing, the validation accuracies are high whenthere is sufficient labeled data for training the proposed NNs.The proposed 2D CNN has the best domain adaptability. TheTrAdaBoost algorithm can help the NNs to train an efficientclassifier that has better domain adaptation. It has been thereforeconcluded that the proposed method, especially the 2D CNN, issuitable for actual distribution networks.
基金sponsored by the U.S. Army Research Laboratory and the U.K. Ministry of Defence and was accomplished under Agreement Number W911NF-06-3-0001
文摘Sensor networks are deployed in many application areas nowadays ranging from environment monitoring, industrial monitoring, and agriculture monitoring to military battlefield sensing. The accuracy of sensor readings is without a doubt one of the most important measures to evaluate the quality of a sensor and its network. Therefore, this work is motivated to propose approaches that can detect and repair erroneous （i.e., dirty） data caused by inevitable system problems involving various hardware and software components of sensor networks. As information about a single event of interest in a sensor network is usually reflected in multiple measurement points, the inconsistency among multiple sensor measurements serves as an indicator for data quality problem. The focus of this paper is thus to study methods that can effectively detect and identify erroneous data among inconsistent observations based on the inherent structure of various sensor measurement series from a group of sensors. Particularly, we present three models to characterize the inherent data structures among sensor measurement traces and then apply these models individually to guide the error detection of a sensor network. First, we propose a multivariate Gaussian model which explores the correlated data changes of a group of sensors. Second, we present a Principal Component Analysis （PCA） model which captures the sparse geometric relationship among sensors in a network. The PCA model is motivated by the fact that not all sensor networks have clustered sensor deployment and clear data correlation structure. Further, if the sensor data show non-linear characteristic, a traditional PCA model can not capture the data attributes properly. Therefore, we propose a third model which utilizes kernel functions to map the original data into a high dimensional feature space and then apply PCA model on the mapped linearized data. All these three models serve the purpose of capturing the underlying phenomenon of a sensor network from its global view, and th
基金supported by the Fundamental Research Funds for the Central Universities(2021RC239)the Postdoctoral Science Foundation of China(2021 M690338)+3 种基金the Hainan Provincial Natural Science Foundation of China(620RC562,2019RC096,620RC560)the Scientific Research Setup Fund of Hainan University(KYQD(ZR)1877)the Program of Hainan Association for Science and Technology Plans to Youth R&D Innovation(QCXM201910)the National Natural Science Foundation of China(61802092,62162021).
文摘With the increasing number of switches in Software-Defined Network-ing(SDN),there are more and more faults rising in the data plane.However,due to the existence of link redundancy and multi-path forwarding mechanisms,these problems cannot be detected in time.The current faulty path detection mechan-isms have problems such as the large scale of detection and low efficiency,which is difficult to meet the requirements of efficient faulty path detection in large-scale SDN.Concerning this issue,we propose an efficient network path fault testing model ProbD based on probability detection.This model achieves a high prob-ability of detecting arbitrary path fault in the form of small-scale random sam-pling.Under a certain path fault rate,ProbD obtains the curve of sample size and probability of detecting arbitrary path fault by randomly sampling network paths several times.After a small number of experiments,the ProbD model can cor-rectly estimate the path fault rate of the network and calculate the total number of paths that need to be detected according to the different probability of detecting arbitrary path fault and the path fault rate of the network.Thefinal experimental results show that,compared with the full path coverage test,the ProbD model based on probability detection can achieve efficient network testing with less overhead.Besides,the larger the network scale is,the more overhead will be saved.
文摘This paper describes the method of built-in self-repairing of RAM on board, designs hardware circuit, and logic for the RAM's faults self-repairing system based on FPGA. The key technology is that it utilizes FPGA to test RAM according to some algorithm to find out failure memory units and replace the faulty units with FPGA. Then it can build a memory that has no fault concern to external controller, and realizes the logic binding between external controller and RAM. Micro Controller Unit (MCU) can operate external RAM correctly even if RAM has some fault address units. Conventional MCS-51 is used to simulate the operation of MCU operating external memory. Simulation shows FPGA can complete the faulty address units' mapping and MCU can normally read and write external RAM. This design realizes the RAM's built-in self-repairing on board.
基金Project supported by the Funds for Creative Research Groups of China(Grant No.60821063)the National Basic Research Program of China(Grant No.2009CB320604)+2 种基金the National Natural Science Foundation of China(Grant No.60974043)the 111 Project(Grant No.B08015)the Science and Technology Research Project of the Educational Department of Liaoning Province of China(Grant No.2008S156)
文摘This paper presents a new robust adaptive synchronization method for a class of uncertain dynamical complex networks with network failures and coupling time-varying delays. Adaptive schemes are proposed to adjust controller parameters for the faulty network compensations, as well as to estimate the upper and lower bounds of delayed state errors and perturbations to compensate the effects of delay and perturbation on-line without assuming symmetry or irreducibility of networks. It is shown that, through Lyapunov stability theory, distributed adaptive controllers con- structed by the adaptive schemes are successful in ensuring the achievement of asymptotic synchronization of networks in the present of faulty and delayed networks, and perturbation inputs. A Chua＇s circuit network example is finally given to show the effectiveness of the proposed synchronization criteria.
文摘In the first part of this- paper, three generalizations of arrangement graph A.,k of [1], namely Bn,k, Cn,k and Dn,k , are introduced. We prove that all the three classes of graphs are vertex symmetric, two of them are edge symmetric. They have great faulty tolerance and high connectivity. We give the diameters of B..k and Cn,k, the Hamiltonian cycle of Cn,k and Hamiltonian path of B.,k. We list several open problems, one of them related to the complexity of sorting algorithm on the arrangement graphs. All these graphs can be thought as generalizations of star graph but are more flexible so that they can be considered as new interconnection network topologies. In the second part of this paper, we provide other four classes of combinatorial graphes, Chn , Cyn, Zhn and Zyn. Many good properties of them, such as high node--connectivity, node symmetry, edge symmetry, diameter, ets., are shown in this paper.
文摘Interfacing DC sources to load/power grid requires DC converters that produce minimum level of current ripples. This is to limit the losses and hence increase the life span of these sources. This article proposes a simple inter-leaved boost converter that interfaces PhotoVoltaic (PV) module into a common DC-link. The article also addresses the faulty mode operation of the proposed circuit while advising the appropriate remedy actions. A MATLAB and Simulink dynamic platform are used to simulate the transient performance of the proposed converter. The results revealed the effectiveness and the viability of the proposed converter in reducing the ripples in the PV current without employing bulky input inductors or increasing the switching frequency.
基金NSF of USA under grant CCR 99O0646 and grant ANI 0073736.
文摘We propose a fault-tolerant tree-based multicast algorithm for 2-dimensional (2-D) meshes based on the concept of the extended safety level which is a vector associated with each node to capture fault information in the neighborhood. In this approach each destination is reached through a minimum number of hops. In order to minimize the total number of traffic steps, three heuristic strategies are proposed. This approach can be easily implemented by pipelined circuit switching (PCS). A simulation study is conducted to measure the total number of traffic steps under different strategies. Our approach is the first attempt to address the fault- tolerant tree-based multicast problem in 2-D meshes based on limited global information with a simple model and succinct information.
文摘Fault management study in smart grid systems (SGSs) is important to ensure the stability of the system. Also, it is important to know the major types of power failures for the effective operation of the SGS. This paper reviews diverse types of faults that might appear in the SGS and gives a survey about the impact of renewable energy resources (RERs) on the behavior of the system. Moreover, this paper offers different fault detection and localization techniques that can be used for SGSs. Furthermore, a potential fault management case study is proposed in this paper. The SGS model in this paper is investigated using both of the Matlab/Simulink and the Real Time Digital Simulation (RTDS) to compute the fault management study. Simulation results show the fast response to a power failure in the system which improves the stability of the SGS.
