The Experts below are selected from a list of 6147 Experts worldwide ranked by ideXlab platform
Huaguang Zhang - One of the best experts on this subject based on the ideXlab platform.
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Multiple Open-Circuit Fault Diagnosis Based on Multistate Data Processing and Subsection Fluctuation Analysis for Photovoltaic Inverter
IEEE Transactions on Instrumentation and Measurement, 2018Co-Authors: Zhanjun Huang, Zhanshan Wang, Huaguang ZhangAbstract:In this paper, a practical fault Diagnosis Algorithm is presented to realize multiple open-circuit fault Diagnosis for photovoltaic (PV) inverters. By the feature analysis for the output currents of normal and fault states, a fault Diagnosis Algorithm is developed, which is composed of multistate data processing (MSDP) block, subsection fluctuation analysis (SSFA) block, and the artificial neural network (ANN) block. First, the MSDP block is used to distinguish the different feature data and to adopt the data processing scheme, which improves the smoothness of the main data, retains the main fault features, and removes the influence of load change. Second, the SSFA block is used to extract the data feature, which can be used to accurately distinguish the different states of any switch. Finally, ANN is used by combining with the proposed MSDP and SSFA to implement intelligent classification, in which the dependency and the number of thresholds can be reduced. Comparing with the existing fault classification Algorithms, the proposed Algorithm is simple and stable to realize the multiple switches fault Diagnosis for PV inverters. It does not require additional hardware equipment, and it can reduce the complexity of design and realization. Finally, the effectiveness of the fault Diagnosis Algorithm is verified by the experimental results.
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A Diagnosis Algorithm for Multiple Open-Circuited Faults of Microgrid Inverters Based on Main Fault Component Analysis
IEEE Transactions on Energy Conversion, 2018Co-Authors: Zhanjun Huang, Zhanshan Wang, Huaguang ZhangAbstract:This paper presents a new open-circuit fault Diagnosis Algorithm for multiple switch of microgrid inverter in different load change condition. From the analysis of the waveform features and frequency composition for the main fault component under the different switch states, a low frequency sampling principle of the main fault component is developed. For the proposed fault Diagnosis Algorithm, first, based on above principle, small sampling data are obtained by the second low frequency mean sample processing, which can be used to reflect the main component features of different switch states and reduce the amount of the processed data. Because the data of the second low frequency mean sample processing comes from the obtained system data, it does not add any additional hardware and has the lower cost. Moreover, the obtained low frequency sampling data are divided into different feature data to adopt corresponding data processing scheme. Thus, the information of the main fault component is retained and the impact of load change is removed for any feature data. Furthermore, the feature values of the processed low frequency sampling data are extracted from the viewpoint of data attenuation. Finally, neural network is used to implement intelligent classification. The dependency and the number of threshold can be reduced, and low frequency sampling data are fully utilized to reflect the overall features. Compared with the existing fault Diagnosis Algorithms, the actual computational quantity is effectively reduced without affecting the accuracy and stability of the Diagnosis results for microgrid inverter under load change condition. Finally, the effectiveness of fault Diagnosis Algorithm is verified through the detailed simulation and experiment results.
Xiaofan Yang - One of the best experts on this subject based on the ideXlab platform.
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A (4n - 9)/3 Diagnosis Algorithm for generalised cube networks
International Journal of Parallel Emergent and Distributed Systems, 2010Co-Authors: Hui Yang, Xiaofan Yang, Amiya NayakAbstract:The goal of the t/k-Diagnosis is to isolate all faulty processors (nodes) in a multiprocessor system to within a set of nodes in which at most k nodes are correct, provided the number of faulty nodes does not exceed t. As compared to the classical precise Diagnosis strategy, the t/k-Diagnosis strategy can significantly improve the self-diagnosing capability of multiprocessor system. The generalised cube network (GCN), or equivalently the BC graphs, is a regular topology, which provides a unified view of the hypercube and some of its variants. This paper addressed the t/k-Diagnosis of GCNs. By exploring the relationship between the size of a largest connected component of the 0-test subgraph of a faulty GCN and the distribution of the faulty nodes over the network, an [image omitted] time (4n - 9)/3 Diagnosis Algorithm on an n-dimensional GCN is presented, where N = 2n is the total number of the nodes of the network being diagnosed. To our knowledge, this is the first time to give a t/k-Diagnosis Algorithm for GCNs and [image omitted] .
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A (4n-9)/3 Diagnosis Algorithm on n-dimensional cube network
Information Sciences, 2007Co-Authors: Xiaofan Yang, Yuan Yan TangAbstract:As a generalization of the precise and pessimistic Diagnosis strategies of system-level Diagnosis of multicomputers, the t/k Diagnosis strategy can significantly improve the self-diagnosing capability of a system at the expense of no more than k fault-free processors (nodes) being mistakenly diagnosed as faulty. In the case k>=2, to our knowledge, there is no known t/k Diagnosis Algorithm for general diagnosable system or for any specific system. Hypercube is a popular topology for interconnecting processors of multicomputers. It is known that an n-dimensional cube is (4n-9)/3-diagnosable. This paper addresses the (4n-9)/3 Diagnosis of n-dimensional cube. By exploring the relationship between a largest connected component of the 0-test subgraph of a faulty hypercube and the distribution of the faulty nodes over the network, the fault Diagnosis of an n-dimensional cube can be reduced to those of two constituent (n-1)-dimensional cubes. On this basis, a Diagnosis Algorithm is presented. Given that there are no more than 4n-9 faulty nodes, this Algorithm can isolate all faulty nodes to within a set in which at most three nodes are fault-free. The proposed Algorithm can operate in O(Nlog"2N) time, where N=2^n is the total number of nodes of the hypercube. The work of this paper provides insight into developing efficient t/k Diagnosis Algorithms for larger k value and for other types of interconnection networks.
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A fast Diagnosis Algorithm for locally twisted cube multiprocessor systems under the MM * model
Computers & Mathematics With Applications, 2007Co-Authors: Hui Yang, Xiaofan YangAbstract:Comparison-based Diagnosis is a practical approach to the system-level fault Diagnosis of multiprocessors. The locally twisted cube is a newly introduced hypercube variant, which not only possesses lower diameter and better graph embedding capability as compared with a hypercube of the same size, but retains some nice properties of hypercubes. This paper addresses the fault Diagnosis of locally twisted cubes under the MM^* comparison model. By utilizing the existence of abundant cycles within a locally twisted cube, we present a new Diagnosis Algorithm. With elaborately organized data, this Algorithm can run in O(Nlog"2^2N) time, where N stands for the total number of nodes. In comparison, the classical Sengupta-Dahbura Diagnosis Algorithm takes as much as O(N^5) time to achieve the same goal. As a consequence, the proposed Algorithm is remarkably superior to the Sengupta-Dahbura Algorithm in terms of the time overhead.
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A comparison-based Diagnosis Algorithm tailored for crossed cube multiprocessor systems
Microprocessors and Microsystems, 2005Co-Authors: Xiaofan Yang, Graham M. Megson, David J. EvansAbstract:Abstract Comparison-based Diagnosis is an effective approach to system-level fault Diagnosis. Under the Maeng–Malek comparison model (MM* model), Sengupta and Dahbura proposed an O(N5) Diagnosis Algorithm for general diagnosable systems with N nodes. Thanks to lower diameter and better graph embedding capability as compared with a hypercube of the same size, the crossed cube has been a promising candidate for interconnection networks. In this paper, we propose a fault Diagnosis Algorithm tailored for crossed cube connected multicomputer systems under the MM* model. By introducing appropriate data structures, this Algorithm runs in O ( N log 2 2 N ) time, which is linear in the size of the input. As a result, this Algorithm is significantly superior to the Sengupta–Dahbura's Algorithm when applied to crossed cube systems.
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A fast pessimistic one-step Diagnosis Algorithm for hypercube multicomputer systems
Journal of Parallel and Distributed Computing, 2004Co-Authors: Xiaofan YangAbstract:This paper describes a system-level Diagnosis Algorithm for hypercube multicomputer systems. The Algorithm is based on the PMC model and can isolate all faulty processors to within a set that contains at most one fault-free processor. If we denote by N the total number of processors in a hypercube system to be diagnosed, then, based on the judiciously designed data structures, the Algorithm can run in O(N log2 N) time: whereas the best-known Diagnosis Algorithm, the YML Algorithm, runs in O(N2.5) time. Consequently, the new Algorithm is remarkably superior to the YML Algorithm in terms of the time cost.
Zhanjun Huang - One of the best experts on this subject based on the ideXlab platform.
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A Practical Fault Diagnosis Algorithm Based on Aperiodic Corrected-Second Low-Frequency Processing for Microgrid Inverter
IEEE Transactions on Industrial Informatics, 2019Co-Authors: Zhanjun Huang, Zhanshan WangAbstract:For most existing aperiodic fault Diagnosis Algorithms of microgrid inverter, because of the common aperiodic processing features, they have relatively higher amount of Algorithm startup, calculation, and complexity. These features increase the hardware requirements and realization difficulty, greatly affect the practicability. In order to improve above-mentioned problems, a practical fault Diagnosis Algorithm is investigated. In this paper, first, aperiodic corrected-second low-frequency processing method is proposed to get aperiodic small low-frequency data (ASLFD) by a simple way in the real time, which greatly reduces the amount of Algorithm startup and corresponding calculation. Second, these ASLFD are processed by the real-time normalization method. Next, the degree of asymmetry and distortion degree of root mean square are extracted, respectively. Furthermore, the feature variables and results are realized through the logical operations. Compared with the existing fault Diagnosis Algorithms, the proposed Algorithm has lower amount of startup and calculation, smaller complexity, and easy realization, which are conducive to practical applications. The detailed experimental results and comparisons are shown to validate the proposed Algorithm.
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Multiple Open-Circuit Fault Diagnosis Based on Multistate Data Processing and Subsection Fluctuation Analysis for Photovoltaic Inverter
IEEE Transactions on Instrumentation and Measurement, 2018Co-Authors: Zhanjun Huang, Zhanshan Wang, Huaguang ZhangAbstract:In this paper, a practical fault Diagnosis Algorithm is presented to realize multiple open-circuit fault Diagnosis for photovoltaic (PV) inverters. By the feature analysis for the output currents of normal and fault states, a fault Diagnosis Algorithm is developed, which is composed of multistate data processing (MSDP) block, subsection fluctuation analysis (SSFA) block, and the artificial neural network (ANN) block. First, the MSDP block is used to distinguish the different feature data and to adopt the data processing scheme, which improves the smoothness of the main data, retains the main fault features, and removes the influence of load change. Second, the SSFA block is used to extract the data feature, which can be used to accurately distinguish the different states of any switch. Finally, ANN is used by combining with the proposed MSDP and SSFA to implement intelligent classification, in which the dependency and the number of thresholds can be reduced. Comparing with the existing fault classification Algorithms, the proposed Algorithm is simple and stable to realize the multiple switches fault Diagnosis for PV inverters. It does not require additional hardware equipment, and it can reduce the complexity of design and realization. Finally, the effectiveness of the fault Diagnosis Algorithm is verified by the experimental results.
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A Diagnosis Algorithm for Multiple Open-Circuited Faults of Microgrid Inverters Based on Main Fault Component Analysis
IEEE Transactions on Energy Conversion, 2018Co-Authors: Zhanjun Huang, Zhanshan Wang, Huaguang ZhangAbstract:This paper presents a new open-circuit fault Diagnosis Algorithm for multiple switch of microgrid inverter in different load change condition. From the analysis of the waveform features and frequency composition for the main fault component under the different switch states, a low frequency sampling principle of the main fault component is developed. For the proposed fault Diagnosis Algorithm, first, based on above principle, small sampling data are obtained by the second low frequency mean sample processing, which can be used to reflect the main component features of different switch states and reduce the amount of the processed data. Because the data of the second low frequency mean sample processing comes from the obtained system data, it does not add any additional hardware and has the lower cost. Moreover, the obtained low frequency sampling data are divided into different feature data to adopt corresponding data processing scheme. Thus, the information of the main fault component is retained and the impact of load change is removed for any feature data. Furthermore, the feature values of the processed low frequency sampling data are extracted from the viewpoint of data attenuation. Finally, neural network is used to implement intelligent classification. The dependency and the number of threshold can be reduced, and low frequency sampling data are fully utilized to reflect the overall features. Compared with the existing fault Diagnosis Algorithms, the actual computational quantity is effectively reduced without affecting the accuracy and stability of the Diagnosis results for microgrid inverter under load change condition. Finally, the effectiveness of fault Diagnosis Algorithm is verified through the detailed simulation and experiment results.
Supriya Kelkar - One of the best experts on this subject based on the ideXlab platform.
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ICACCI - Coordinator-based adaptive fault Diagnosis Algorithm for distributed computing systems
2017 International Conference on Advances in Computing Communications and Informatics (ICACCI), 2017Co-Authors: Supriya Kelkar, Deepali G. Yeole, Mayuri B. Sinkar, Priyanka B. Jagtap, Damini S. ZagadeAbstract:Distributed computing system consists of number of independent computers located at different locations. These computers may become faulty or unresponsive at any point of time. Such failures will affect the overall performance of the distributed system. Thus, it becomes necessary to detect such faulty computers or nodes. This paper proposes a new adaptive fault Diagnosis Algorithm namely, Coordinator-based Adaptive Fault Diagnosis Algorithm for distributed systems. It uses an innovative technique where a coordinator pair supervises and detects the faulty nodes in the system. This is an adaptive fault Diagnosis Algorithm for t-diagnosable system, where ‘t’ is the upper bound for the number of detectable faulty nodes in the system. This Algorithm also allows re-entry of repaired faulty nodes in the next diagnostic cycle. This Algorithm is periodically executed at every node to detect the faulty nodes in the distributed computer network. The implementation of the proposed Algorithm on a small laboratory setup is presented in this paper along with the results.
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adaptive fault Diagnosis Algorithm for controller area network
IEEE Transactions on Industrial Electronics, 2014Co-Authors: Supriya Kelkar, Raj KamalAbstract:A controller area network (CAN)-based distributed system may develop faults at run-time. These faults need to be detected and diagnosed. This paper proposes a new Algorithm named adaptive fault Diagnosis Algorithm for CAN (AFDCAN). It is designed for low-cost resource-constrained distributed embedded systems. The proposed Algorithm detects all faulty nodes on the CAN. It allows new node entry and reentry of repaired faulty nodes during a diagnostic cycle. AFDCAN is found to provide high fault tolerance and to ensure reliable communication. It uses single-channel communication deploying the bus-based standard CAN protocol. A hardware implementation of the proposed Algorithm has been used to obtain the results. The results show that the proposed Algorithm diagnoses all faults in the system. Analysis of the proposed Algorithm proves that the Algorithm uses a definite and bounded number of testing rounds and messages to complete one diagnostic cycle.
Raj Kamal - One of the best experts on this subject based on the ideXlab platform.
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adaptive fault Diagnosis Algorithm for controller area network
IEEE Transactions on Industrial Electronics, 2014Co-Authors: Supriya Kelkar, Raj KamalAbstract:A controller area network (CAN)-based distributed system may develop faults at run-time. These faults need to be detected and diagnosed. This paper proposes a new Algorithm named adaptive fault Diagnosis Algorithm for CAN (AFDCAN). It is designed for low-cost resource-constrained distributed embedded systems. The proposed Algorithm detects all faulty nodes on the CAN. It allows new node entry and reentry of repaired faulty nodes during a diagnostic cycle. AFDCAN is found to provide high fault tolerance and to ensure reliable communication. It uses single-channel communication deploying the bus-based standard CAN protocol. A hardware implementation of the proposed Algorithm has been used to obtain the results. The results show that the proposed Algorithm diagnoses all faults in the system. Analysis of the proposed Algorithm proves that the Algorithm uses a definite and bounded number of testing rounds and messages to complete one diagnostic cycle.