Granger Causality for prediction in Dynamic Mode Decomposition: Application to power systems

Gunjal, Revati; Nayyer, Syed Shadab; Wagh, S.R.; Stankovic, A.M.; Singh, N.M.

doi:10.1016/j.epsr.2023.109865

Cited by 5 publications

(3 citation statements)

References 33 publications

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“…The GCT serves as a valuable tool for discerning causal relationships among key factors [17]. This test is popular in several fields, including economics and climate change studies [18]. For instance, the GCT was employed by Dörgő et al [19] to measure the interconnectedness among the United Nations sustainability goals, identifying causal relationships represented in a network of sustainability indicators.…”

Section: Estimation and Testing Sustainability Indexmentioning

confidence: 99%

Dam Sustainability’s Interdependency with Climate Change and Dam Failure Drivers

Abu-Afifeh,

Rahbeh,

Al-Afeshat

et al. 2023

Sustainability

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The complex interplay between the drivers of dam failure and climate change factors constitutes a central focus of this study. Variations in temperature and precipitation patterns have profound effects on dam storage volume, quality, and structural stability. This research serves two purposes. First, it employs the Wroclaw Taxonomic Method (WTM) to construct a comprehensive indicator called the Index of Sustainable Dams (ISD). The King Talal Dam (KTD) was chosen as a case study using data spanning from 2011 to 2022, encompassing 14 variables. Second, the Granger Causality Test (GCT) is applied to examine the causal connections between the ISD, climate change, and dam failure. The research provides an in-depth evaluation of the KTD in terms of the ISD and its three critical dimensions: environmental, economic, and social. It also reveals substantial bidirectional causality relationships between the ISD, climate change (as measured by mean maximum and minimum temperature and mean annual precipitation), and dam failure (as measured by dam age, sediment yield, and storage). The analysis confirms a strong interdependence among the drivers of climate change, dam failure, and dam sustainability. Moreover, combining the WTM and the GCT is efficient for assessing the sustainability of various dam types.

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Section: Estimation and Testing Sustainability Indexmentioning

confidence: 99%

Dam Sustainability’s Interdependency with Climate Change and Dam Failure Drivers

Abu-Afifeh,

Rahbeh,

Al-Afeshat

et al. 2023

Sustainability

View full text Add to dashboard Cite

show abstract

“…These modes describe the system's evolution behavior, approximating the spectrum of the Koopman operator through a matrix. The primary eigenvalues and eigenvectors of this matrix provide detailed information about the system's dynamic properties, including frequency, decay, growth, and flow patterns [5]. First proposed by Schmid [6] in 2010, DMD has found extensive applications across different fields such as fluid dynamics [7], power systems [5], and meteorology [8].…”

Section: Introductionmentioning

confidence: 99%

“…The primary eigenvalues and eigenvectors of this matrix provide detailed information about the system's dynamic properties, including frequency, decay, growth, and flow patterns [5]. First proposed by Schmid [6] in 2010, DMD has found extensive applications across different fields such as fluid dynamics [7], power systems [5], and meteorology [8]. It involves matrix decomposition of time-series data to extract dynamic modes, which are then used to predict future system behavior.…”

Section: Introductionmentioning

confidence: 99%

Data-Driven Modal Decomposition Methods as Feature Detection Techniques for Flow Fields in Hydraulic Machinery: A Mini Review

Xu,

Zhang,

Zhang

et al. 2024

JMSE

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Cavitation is a quasi-periodic process, and its non-stationarity leads to increasingly complex flow field structures. On the other hand, characterizing the flow field with greater precision has become increasingly feasible. However, accurately and effectively extracting the most representative vibration modes and spatial structures from these vast amounts of data has become a significant challenge. Researchers have proposed data-driven modal decomposition techniques to extract flow field information, which have been widely applied in various fields such as signal processing and fluid dynamics. This paper addresses the application of modal decomposition methods, such as dynamic mode decomposition (DMD), Proper Orthogonal Decomposition (POD), and Spectral Proper Orthogonal Decomposition (SPOD), in cavitation feature detection in hydraulic machinery. It reviews the mathematical principles of these three algorithms and a series of improvements made by researchers since their inception. It also provides examples of the applications of these three algorithms in different hydraulic machinery. Based on this, the future development trends and possible directions for the improvement of modal decomposition methods are discussed.

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Linear and nonlinear Granger causality analysis of turbulent duct flows

Lopez-Doriga,

Atzori,

Vinuesa

et al. 2024

J. Phys.: Conf. Ser.

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This research focuses on the identification and causality analysis of coherent structures that arise in turbulent flows in square and rectangular ducts. Coherent structures are first identified from direct numerical simulation data via proper orthogonal decomposition (POD), both by using all velocity components, and after separating the streamwise and secondary components of the flow. The causal relations between the mode coefficients are analysed using pairwise-conditional Granger causality analysis. We also formulate a nonlinear Granger causality analysis that can account for nonlinear interactions between modes. Focusing on streamwise-constant structures within a duct of short streamwise extent, we show that the causal relationships are highly sensitive to whether the mode coefficients or their squared values are considered, whether nonlinear effects are explicitly accounted for, and whether streamwise and secondary flow structures are separated prior to causality analyses. We leverage these sensitivities to determine that linear mechanisms underpin causal relationships between modes that share the same symmetry or anti-symmetry properties about the corner bisector, while nonlinear effects govern the causal interactions between symmetric and antisymmetric modes. In all cases, we find that the secondary flow fluctuations (manifesting as streamwise vorticial structures) are the primary cause of both the presence and movement of near-wall streaks towards and away from the duct corners.

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Granger Causality for prediction in Dynamic Mode Decomposition: Application to power systems

Cited by 5 publications

References 33 publications

Dam Sustainability’s Interdependency with Climate Change and Dam Failure Drivers

Dam Sustainability’s Interdependency with Climate Change and Dam Failure Drivers

Data-Driven Modal Decomposition Methods as Feature Detection Techniques for Flow Fields in Hydraulic Machinery: A Mini Review

Linear and nonlinear Granger causality analysis of turbulent duct flows

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