The Experts below are selected from a list of 1059 Experts worldwide ranked by ideXlab platform
Sirkka-liisa Jämsä-jounela - One of the best experts on this subject based on the ideXlab platform.
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CDC - Data Characterization for automatic selection of valve stiction detection algorithms
52nd IEEE Conference on Decision and Control, 2013Co-Authors: Alexey Zakharov, Elena Zattoni, Octavio Pozo, Sirkka-liisa Jämsä-jounelaAbstract:This paper proposes a valve stiction detection system which selects applicable valve stiction detection algorithms based on Characterization of the Data. Additionally, the proposed system computes the final detection decision, weighting, by means of suitably-defined reliability indexes, the individual decisions provided by the selected algorithms. The paper demonstrates the effectiveness of the proposed valve stiction detection system with benchmark industrial Data.
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An autonomous valve stiction detection system based on Data Characterization
Control Engineering Practice, 2013Co-Authors: Alexey Zakharov, Elena Zattoni, Octavio Pozo Garcia, Sirkka-liisa Jämsä-jounelaAbstract:Abstract This paper proposes a valve stiction detection system which selects valve stiction detection algorithms based on Characterizations of the Data. For this purpose, novel Data feature indexes are proposed, which quantify the presence of oscillations, mean-nonstationarity, noise and nonlinearities in a given Data sequence. The selection is then performed according to the conditions on the index values in which each method can be applied successfully. Finally, the stiction detection decision is given by combining the detection decisions made by the selected methods. The paper ends demonstrating the effectiveness of the proposed valve stiction detection system with benchmark industrial Data.
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Data Characterization for automatic selection of valve stiction detection algorithms
52nd IEEE Conference on Decision and Control, 2013Co-Authors: Alexey Zakharov, Elena Zattoni, Octavio Pozo, Sirkka-liisa Jämsä-jounelaAbstract:This paper proposes a valve stiction detection system which selects applicable valve stiction detection algorithms based on Characterization of the Data. Additionally, the proposed system computes the final detection decision, weighting, by means of suitably-defined reliability indexes, the individual decisions provided by the selected algorithms. The paper demonstrates the effectiveness of the proposed valve stiction detection system with benchmark industrial Data.
Eduard Muljadi - One of the best experts on this subject based on the ideXlab platform.
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Spatial-Temporal Synchrophasor Data Characterization and Analytics in Smart Grid Fault Detection, Identification, and Impact Causal Analysis
IEEE Transactions on Smart Grid, 2016Co-Authors: Huaiguang Jiang, David Wenzhong Gao, Xiaoxiao Dai, Jun Jason Zhang, Yunsheng Zhang, Eduard MuljadiAbstract:An approach of Big Data Characterization for smart grids (SGs) and its applications in fault detection, identification and causal impact analysis is proposed in this paper, which aims to provide substantial Data volume reduction while keeping comprehensive information from synchrophasor measurements in spatial and temporal domains. Specially, based on secondary voltage control (SVC) and local SG observation algorithm, a two-layer dynamic optimal synchrophasor measurement devices selection algorithm (OSMDSA) is proposed to determine SVC zones, their corresponding pilot buses and the optimal synchrophasor measurement devices. Combining the two-layer dynamic OSMDSA and matching pursuit decomposition (MPD), the synchrophasor Data is completely characterized in the spatial-temporal domain. To demonstrate the effectiveness of the proposed Characterization approach, smart grid situational awareness is investigated based on hidden Markov model (HMM) based fault detection and identification using the spatial-temporal characteristics generated from the reduced Data. To identify the major impact buses, the weighted Granger causality (WGC) for SGs is proposed to investigate the causal relationship of buses during system disturbance. The IEEE 39-bus system and IEEE 118-bus system are employed to validate and evaluate the proposed approach.
Alexey Zakharov - One of the best experts on this subject based on the ideXlab platform.
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CDC - Data Characterization for automatic selection of valve stiction detection algorithms
52nd IEEE Conference on Decision and Control, 2013Co-Authors: Alexey Zakharov, Elena Zattoni, Octavio Pozo, Sirkka-liisa Jämsä-jounelaAbstract:This paper proposes a valve stiction detection system which selects applicable valve stiction detection algorithms based on Characterization of the Data. Additionally, the proposed system computes the final detection decision, weighting, by means of suitably-defined reliability indexes, the individual decisions provided by the selected algorithms. The paper demonstrates the effectiveness of the proposed valve stiction detection system with benchmark industrial Data.
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An autonomous valve stiction detection system based on Data Characterization
Control Engineering Practice, 2013Co-Authors: Alexey Zakharov, Elena Zattoni, Octavio Pozo Garcia, Sirkka-liisa Jämsä-jounelaAbstract:Abstract This paper proposes a valve stiction detection system which selects valve stiction detection algorithms based on Characterizations of the Data. For this purpose, novel Data feature indexes are proposed, which quantify the presence of oscillations, mean-nonstationarity, noise and nonlinearities in a given Data sequence. The selection is then performed according to the conditions on the index values in which each method can be applied successfully. Finally, the stiction detection decision is given by combining the detection decisions made by the selected methods. The paper ends demonstrating the effectiveness of the proposed valve stiction detection system with benchmark industrial Data.
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Data Characterization for automatic selection of valve stiction detection algorithms
52nd IEEE Conference on Decision and Control, 2013Co-Authors: Alexey Zakharov, Elena Zattoni, Octavio Pozo, Sirkka-liisa Jämsä-jounelaAbstract:This paper proposes a valve stiction detection system which selects applicable valve stiction detection algorithms based on Characterization of the Data. Additionally, the proposed system computes the final detection decision, weighting, by means of suitably-defined reliability indexes, the individual decisions provided by the selected algorithms. The paper demonstrates the effectiveness of the proposed valve stiction detection system with benchmark industrial Data.
Swades De - One of the best experts on this subject based on the ideXlab platform.
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An Efficient Data Characterization and Reduction Scheme for Smart Metering Infrastructure
IEEE Transactions on Industrial Informatics, 2018Co-Authors: Sharda Tripathi, Swades DeAbstract:In this paper, a novel Characterization of smart meter Data based on Gaussian mixture (GM) model is presented. It is shown that compared to the existing Characterization models, the proposed GM model provides a significantly better fit for smart meter Data. Furthermore, at each smart meter, sparsity of Data is exploited to devise an adaptive Data reduction algorithm using compressive sampling technique such that the bandwidth requirement for smart meter Data transmission is reduced with minimum loss of information. When compared to the closest competitive scheme, the proposed compressive sampling based Data reduction algorithm is found to be noise robust and offers ${\text{12.8}}$ % and ${\text{7.4}}$ % higher bandwidth saving, respectively, at 1 s and 30 s sampling intervals for comparable reconstruction accuracy. Proposed scheme is tested in real-time using RT-LAB.
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An Efficient Data Characterization and Reduction Scheme for Smart Metering Infrastructure
IEEE Transactions on Industrial Informatics, 2018Co-Authors: Sharda Tripathi, Swades DeAbstract:In this paper, a novel Characterization of smart meter Data based on Gaussian mixture (GM) model is presented. It is shown that compared to the existing Characterization models, the proposed GM model provides a significantly better fit for smart meter Data. Furthermore, at each smart meter, sparsity of Data is exploited to devise an adaptive Data reduction algorithm using compressive sampling technique such that the bandwidth requirement for smart meter Data transmission is reduced with minimum loss of information. When compared to the closest competitive scheme, the proposed compressive sampling based Data reduction algorithm is found to be noise robust and offers 12.8% and 7.4% higher bandwidth saving, respectively, at 1 s and 30 s sampling intervals for comparable reconstruction accuracy. Proposed scheme is tested in real-time using RT-LAB.
Huaiguang Jiang - One of the best experts on this subject based on the ideXlab platform.
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Spatial-Temporal Synchrophasor Data Characterization and Analytics in Smart Grid Fault Detection, Identification, and Impact Causal Analysis
IEEE Transactions on Smart Grid, 2016Co-Authors: Huaiguang Jiang, David Wenzhong Gao, Xiaoxiao Dai, Jun Jason Zhang, Yunsheng Zhang, Eduard MuljadiAbstract:An approach of Big Data Characterization for smart grids (SGs) and its applications in fault detection, identification and causal impact analysis is proposed in this paper, which aims to provide substantial Data volume reduction while keeping comprehensive information from synchrophasor measurements in spatial and temporal domains. Specially, based on secondary voltage control (SVC) and local SG observation algorithm, a two-layer dynamic optimal synchrophasor measurement devices selection algorithm (OSMDSA) is proposed to determine SVC zones, their corresponding pilot buses and the optimal synchrophasor measurement devices. Combining the two-layer dynamic OSMDSA and matching pursuit decomposition (MPD), the synchrophasor Data is completely characterized in the spatial-temporal domain. To demonstrate the effectiveness of the proposed Characterization approach, smart grid situational awareness is investigated based on hidden Markov model (HMM) based fault detection and identification using the spatial-temporal characteristics generated from the reduced Data. To identify the major impact buses, the weighted Granger causality (WGC) for SGs is proposed to investigate the causal relationship of buses during system disturbance. The IEEE 39-bus system and IEEE 118-bus system are employed to validate and evaluate the proposed approach.
