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Abstract Identifying and understanding various causal relations are fundamental to climate dynamics for improving the predictive capacity of Earth system modeling. In particular, causality in Earth systems has manifest temporal periodicities, like physical climate variabilities. To unravel the characteristic frequency of causality in climate dynamics, we develop a data‐analytic framework based on a combination of causality detection and Hilbert spectral analysis, using a long‐term temperature and precipitation dataset in the contiguous United States. Using the Huang–Hilbert transform, we identify the intrinsic frequencies of cross‐regional causality for precipitation and temperature, ranging from interannual to interdecadal time scales. In addition, we analyze the spectra of the physical climate variabilities, including El Niño‐Southern Oscillation and Pacific Decadal Oscillation. It is found that the intrinsic causal frequencies are positively associated with the physics of the oscillations in the global climate system. The proposed methodology provides fresh insights into the causal connectivity in Earth's hydroclimatic system and its underlying mechanism as regulated by the characteristic low‐frequency variability associated with various climatic dynamics.
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Persistent-homology-based detection of power system low-frequency oscillations using PMUs
This paper presents a new methodology to detect low-frequency oscillations in power grids by use of timesynchronized data from phasor measurement units (PMUs). Principal component analysis (PCA) is first applied to the massive PMU data to extract the low-dimensional features, i.e., the principal components (PCs). Then, based on persistent homology, a cyclicity response function is proposed to detect low-frequency oscillations through the use of PCs. Whenever the cyclicity response exceeds a numerically robust threshold, a low-frequency oscillation can be detected instantly. Such swift detection can then be followed by modal analysis tools for more detailed information about the oscillation. Numerical examples using real data illustrate the effectiveness of the proposed methodology for quick detection of oscillations during operations.
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- PAR ID:
- 10037667
- Date Published:
- Journal Name:
- 2016 IEEE Symposium on Signal and Information Processing for Smart Grid Infrastructures (GlobalSIP)
- Page Range / eLocation ID:
- 796 to 800
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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- Free Publicly Accessible Full Text
-
Accepted Manuscript1.0
- Conference Paper:
- https://doi.org/10.1109/GlobalSIP.2016.7905952
