Spatial-Temporal Synchrophasor Data Characterization and Analytics in Smart Grid Fault Detection, Identification, and Impact Causal Analysis
Abstract
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. Especially, 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, the synchrophasor data is completely characterized in the spatial-temporal domain. To demonstrate the effectiveness of the proposed characterization approach, SG situational awareness is investigated based on hidden Markov model 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 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.
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Causal reasoning is useful in many AIoT applications such as online recommendation in smart e-learning [226], fault analysis in smart grids [230], and driving safety in smart transportation [231].
“Causal reasoning is useful in many AIoT applications such as online recommendation in smart e-learning [226], fault analysis in smart grids [230], and driving safety in smart transportation [231].”
From Empowering things with intelligence: a survey of the progress, challenges, and opportunities in artificial intelligence of things (2021) · cites this paper as [230]
Knowledge representation and causal relationship discovery are also explored for power grid fault diagnosis and impact causal analysis.
“Besides, knowledge representation [228] and causal relationship discovery [230] are also explored for power grid fault diagnosis and impact causal analysis.”
From Empowering things with intelligence: a survey of the progress, challenges, and opportunities in artificial intelligence of things (2021) · cites this paper as [230]
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- Authors
- Jiang, H., Dai, X., Gao, D. W., Zhang, J. J., Zhang, Y., & Muljadi, E.
- Affiliations
- National Laboratory of the Rockies (US)
- University of Denver (US)
- Venue
- IEEE Transactions on Smart Grid
- Published
- 2016-09
- Volume
- 7
- Issue
- 5
- Pages
- 2525-2536
- ISSN
- 1949-3053 (ISSN-L)
- 1949-3061
- OpenAlex
- W2340093701
- Cited by
- 56
- References
- 42
- Citations by year
- 2026: 3 · 2025: 2 · 2023: 6 · 2022: 6 · 2021: 5 · 2020: 7 · 2019: 9 · 2018: 13 · 2017: 4 · 2016: 1
- FWCI
- 4.15
- Type
- journal article
- Language
- en
- Open access
- closed
- Locations
- IEEE Transactions on Smart Grid (publishedVersion)
- Topics
- Physical Sciences › Engineering › Electrical and Electronic Engineering › Power System Optimization and Stability
- Physical Sciences › Engineering › Control and Systems Engineering › Power Systems Fault Detection
- Physical Sciences › Engineering › Control and Systems Engineering › Smart Grid Security and Resilience
- Keywords
- synchrophasor measurements, fault identification, secondary voltage control, matching pursuit decomposition, hidden Markov models, situational awareness, data reduction
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