The DOI matches its Crossref record.
Distribution System Bad Data Detection Using Graph Signal Processing
Abstract
As advanced metering infrastructure becomes increasingly prevalent, new data-driven techniques for distribution system control and monitoring have emerged. The efficacy of these techniques, however, can be compromised by erroneous data. This paper develops a graph signal processing-based bad data detection algorithm. First, a physics-based graph construction algorithm is proposed for three-phase power distribution systems. Then, we introduce a data-driven algorithm for detecting erroneous data points by clustering the low-dimensional representations of the graph Fourier transforms of voltage signals. The numerical study results using the IEEE 13-bus test feeder and real-world smart meter time series data show that our proposed algorithm achieves high F1 and accuracy scores.
Overview
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Claims the paper makes
Claims other papers make about this one
Each is checked against this paper’s own text: does it say what the citing paper says it does?
The prevalence of outliers in modern distribution systems poses a challenge to obtaining reliable power flow solutions.
“Besides, the prevalence of outliers in modern distribution systems poses another challenge to obtaining reliable PF solutions. Anomalous data, recorded by meters, might arise for a variety of reasons, due to meter malfunction, false data injection attacks, or communication interruption [12], [13].”
From Taylor-Expansion-Based Robust Power Flow in Unbalanced Distribution Systems: A Hybrid Data-Aided Method (2024) · cites this paper as [12]
Citations
Cites
No references could be resolved yet.
Research works printed but not linked: 16
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Cited by
Sorted by citations
Chung, S., Zhang, Y., Wang, Z., & Ding, F. (2024) · arXiv · doi:10.48550/arxiv.2407.19253 · Cited by 3
“Anomalous data, recorded by meters, might arise for a variety of reasons, due to meter malfunction, false data injection attacks, or communication interruption [12], [13].”
Background
Metadata
- Authors
- Anderson, O., & Yu, N.
- Affiliations
- University of California, Riverside (US)
- Venue
- 2021 IEEE Power & Energy Society General Meeting (PESGM)
- Publisher
- IEEE
- Published
- 2021-07-26
- Pages
- 01-05
- ISSN
- 1932-5517 (ISSN-L)
- OpenAlex
- W4205812317
- Cited by
- 9
- References
- 0
- Citations by year
- 2024: 3 · 2023: 2 · 2022: 4
- FWCI
- 3.36
- Type
- conference paper
- Language
- en
- Open access
- closed
- Locations
- IEEE Power Engineering Society General Meeting (publishedVersion)
- Topics
- Physical Sciences › Engineering › Control and Systems Engineering › Smart Grid Security and Resilience
- Physical Sciences › Engineering › Electrical and Electronic Engineering › Electricity Theft Detection Techniques
- Physical Sciences › Engineering › Electrical and Electronic Engineering › Power System Optimization and Stability
- Keywords
- bad data detection, distribution systems, graph signal processing, graph Fourier transform, graph construction, advanced metering infrastructure, smart meter data, voltage signals, clustering, three-phase systems, IEEE 13-bus test feeder
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