The data dimensionality reduction and bad data detection in the process of smart grid reconstruction through machine learning Article Swipe
Bo Yu
,
Zheng Wang
,
Shangke Liu
,
Xiaomin Liu
,
Ruixin Gou
·
YOU?
·
· 2020
· Open Access
·
· DOI: https://doi.org/10.1371/journal.pone.0237994
YOU?
·
· 2020
· Open Access
·
· DOI: https://doi.org/10.1371/journal.pone.0237994
To detect false data injection attacks (FDIAs) in power grid reconstruction and solve the problem of high data dimension and bad abnormal data processing in the power system, thereby achieving safe and stable operation of the power grid system, this study introduces machine learning methods to explore the detection of FDIAs. First, through the utilization of the standard IEEE node system and the simulation of FDIAs under the condition of non-complete topology information, the construction of the attack data set is completed, and the MatPower tool is applied to simulate and analyze the data set. Second, based on the isolated Forest (iForest) abnormal score data processing algorithm combined with the Local Linear Embedding (LLE) data dimensionality reduction method, an algorithm for data feature extraction is constructed. Finally, based on the combination of the Convolutional Neural Network (CNN) and the Gated Recurrent Unit (GRU) network, an algorithm model for FDIAs detection is constructed. The results show that in the IEEE14-bus node and IEEE118-bus node systems, the overall distribution of the state estimated before and after the attack vector injection is consistent with the initial value. In the iFores algorithm, the number of iTree and the number of samples affect the extraction of abnormal score data. When the number of iTree n is determined to be 100, and the corresponding number of samples w is determined to be 10, the algorithm has the best detection effect. The FDIAs detection algorithm model based on CNN-GRU shows good detection effects under high attack intensity, with an accuracy rate of more than 95%, and its performance is better than other traditional detection algorithms. In this study, the bad data detection model based on deep learning has an active role in the realization of the safe and stable operation of the smart grid.
Related Topics
Concepts
Dimensionality reduction
Computer science
Data set
Convolutional neural network
Algorithm
Feature extraction
Reduction (mathematics)
Node (physics)
Data modeling
Support vector machine
Data mining
Artificial intelligence
Pattern recognition (psychology)
Mathematics
Engineering
Structural engineering
Database
Geometry
Metadata
- Type
- article
- Language
- en
- Landing Page
- https://doi.org/10.1371/journal.pone.0237994
- OA Status
- gold
- Cited By
- 9
- References
- 28
- Related Works
- 10
- OpenAlex ID
- https://openalex.org/W3091950574
All OpenAlex metadata
Raw OpenAlex JSON
- OpenAlex ID
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https://openalex.org/W3091950574Canonical identifier for this work in OpenAlex
- DOI
-
https://doi.org/10.1371/journal.pone.0237994Digital Object Identifier
- Title
-
The data dimensionality reduction and bad data detection in the process of smart grid reconstruction through machine learningWork title
- Type
-
articleOpenAlex work type
- Language
-
enPrimary language
- Publication year
-
2020Year of publication
- Publication date
-
2020-10-07Full publication date if available
- Authors
-
Bo Yu, Zheng Wang, Shangke Liu, Xiaomin Liu, Ruixin GouList of authors in order
- Landing page
-
https://doi.org/10.1371/journal.pone.0237994Publisher landing page
- Open access
-
YesWhether a free full text is available
- OA status
-
goldOpen access status per OpenAlex
- OA URL
-
https://doi.org/10.1371/journal.pone.0237994Direct OA link when available
- Concepts
-
Dimensionality reduction, Computer science, Data set, Convolutional neural network, Algorithm, Feature extraction, Reduction (mathematics), Node (physics), Data modeling, Support vector machine, Data mining, Artificial intelligence, Pattern recognition (psychology), Mathematics, Engineering, Structural engineering, Database, GeometryTop concepts (fields/topics) attached by OpenAlex
- Cited by
-
9Total citation count in OpenAlex
- Citations by year (recent)
-
2025: 1, 2024: 2, 2023: 1, 2022: 4, 2021: 1Per-year citation counts (last 5 years)
- References (count)
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28Number of works referenced by this work
- Related works (count)
-
10Other works algorithmically related by OpenAlex
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