Uncertainty quantification of proper orthogonal decomposition based online power-distribution reconstruction

Li, Zhuo; Ma, Yu; Cao, Liangzhi; Wu, Hongchun

doi:10.1016/j.anucene.2019.107094

Cited by 4 publications

(2 citation statements)

References 15 publications

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“…See Appendix A, Figures A1-A4, for corresponding details for points Z10-2 to Z10-5. The magnitude of the normalized eigenvector is measured by the eigenvalue, as described in Equation (27). For each moving unit, Mode 1 was the dominant mode during the monitoring period ( σ 2 1 σ 2 2 , σ 2 3 , σ 2 4 , σ 2 5 → 0 ).…”

Section: Monitored Results At Section 10mentioning

confidence: 99%

“…Xiang et al [26] proposed a design and optimization method for an extensive data processing system based on orthogonal decomposition, which has the advantage of performing efficient and hierarchical calculations such that the primary target and secondary factors are interlayered, and the primary target is prioritized. It dramatically accelerates the processing of large datasets and improves its efficiency (Li et al [27]; Li et al [28]; Suzuki et al [29]).…”

Section: Introductionmentioning

confidence: 99%

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A Sliding Windows Singular Decomposition Model of Monitoring Data for Operational Tunnels

Xing

Yao

et al. 2022

Symmetry

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In order to extract the valuable information from massive and usually unstructured datasets, increasingly, a novel nonparametric approach is proposed for detecting early signs of structural deterioration in civil infrastructure systems from vast field-monitoring datasets. The process adopted six-sample sliding window overtime at one-hour time increments to overcome the fact that the sampling times were not precisely consistent at all monitoring points. After data processing by this method, the eigenvalues and eigenvectors were obtained for each moving window, and then an evaluation index was constructed. Monitored tunnel data were analyzed using the proposed method. The required information extracted from an individual moving window is represented by a set of principal components, which become the new orthogonal variables. The resulting evaluation indicator was strongly correlated with measured and calculated values up to 0.89, even for tiny monitoring datasets. Experiments have verified the rationality and effectiveness of the algorithm, which provides a reference for the application of the method in the monitoring data processing.

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Section: Monitored Results At Section 10mentioning

confidence: 99%