Topology and FDIA identification in distribution system state estimation using a data-driven approach
In general, the solution of distribution system state estimation (DSSE) is highly dependent on the accuracy of measurement data and accurate topology data. Since the measurement devices are more vulnerable to different types of cyberattacks such as denial-of-service (DoS) attacks, outliers, and stru...
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| Veröffentlicht in: | Measurement : journal of the International Measurement Confederation Jg. 253; S. 117741 |
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| Sprache: | Englisch |
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01.09.2025
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| ISSN: | 0263-2241 |
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| Abstract | In general, the solution of distribution system state estimation (DSSE) is highly dependent on the accuracy of measurement data and accurate topology data. Since the measurement devices are more vulnerable to different types of cyberattacks such as denial-of-service (DoS) attacks, outliers, and structured false data injection attacks (FDIAs), the accuracy of DSSE is significantly affected. In addition, the topology identification is also affected when the measurements are used for the task of topology detection. To address these issues, a novel denoising autoencoder (DAE) is developed with the use of graph-based temporal convolutional layers in the encoder and decoder stages. The graph-based temporal convolutional DAE (G-TCDAE) utilizes the graph knowledge and temporal convolutional structure to understand the spatial correlations and temporal correlations among the input measurements respectively, which enhances the accuracy of reconstructed data against cyberattacks, and classification tasks of topology and FDIA locations. A DAE model can reconstruct the measurement data when input data are having cyberattacks which are considered as noise. In addition, the same model can identify the correct topology as well as measurements having FDIAs. The G-TCDAE model’s performance is assessed and compared with other types of DAE models by performing simulation works on modified IEEE 13-node and IEEE 37-node distribution test systems. The robustness of the model’s capability is tested at different percentages of cyberattacks and results demonstrated that the G-TCDAE model is highly skillful in achieving efficient performance. Also, the model performance is not significantly affected by the scalability which is tested with the IEEE 123-node distribution system.
•A novel graph-based temporal convolutional DAE is developed for cyberattacks.•The proposed model is utilized for the detection of stealthy FDIAs.•Also, the same model is leveraged for topology identification.•The model’s output is used for the reconstruction of FDIA and missing data.•The model performance is compared with other DAE-based models. |
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| AbstractList | In general, the solution of distribution system state estimation (DSSE) is highly dependent on the accuracy of measurement data and accurate topology data. Since the measurement devices are more vulnerable to different types of cyberattacks such as denial-of-service (DoS) attacks, outliers, and structured false data injection attacks (FDIAs), the accuracy of DSSE is significantly affected. In addition, the topology identification is also affected when the measurements are used for the task of topology detection. To address these issues, a novel denoising autoencoder (DAE) is developed with the use of graph-based temporal convolutional layers in the encoder and decoder stages. The graph-based temporal convolutional DAE (G-TCDAE) utilizes the graph knowledge and temporal convolutional structure to understand the spatial correlations and temporal correlations among the input measurements respectively, which enhances the accuracy of reconstructed data against cyberattacks, and classification tasks of topology and FDIA locations. A DAE model can reconstruct the measurement data when input data are having cyberattacks which are considered as noise. In addition, the same model can identify the correct topology as well as measurements having FDIAs. The G-TCDAE model’s performance is assessed and compared with other types of DAE models by performing simulation works on modified IEEE 13-node and IEEE 37-node distribution test systems. The robustness of the model’s capability is tested at different percentages of cyberattacks and results demonstrated that the G-TCDAE model is highly skillful in achieving efficient performance. Also, the model performance is not significantly affected by the scalability which is tested with the IEEE 123-node distribution system.
•A novel graph-based temporal convolutional DAE is developed for cyberattacks.•The proposed model is utilized for the detection of stealthy FDIAs.•Also, the same model is leveraged for topology identification.•The model’s output is used for the reconstruction of FDIA and missing data.•The model performance is compared with other DAE-based models. |
| ArticleNumber | 117741 |
| Author | Raghuvamsi, Y. Teeparthi, Kiran Batchu, Sreenadh |
| Author_xml | – sequence: 1 givenname: Y. orcidid: 0000-0001-9816-7682 surname: Raghuvamsi fullname: Raghuvamsi, Y. email: vamsi777raghu@lbrce.ac.in organization: Department of Electrical and Electronics Engineering, Lakireddy Bali Reddy College of Engineering, Mylavaram, Andhra Pradesh, 521230, India – sequence: 2 givenname: Sreenadh surname: Batchu fullname: Batchu, Sreenadh email: sreenadh@rguktn.ac.in organization: Department of Electrical and Electronics Engineering, Rajiv Gandhi University of Knowledge Technologies, Nuzvid, Andhra Pradesh, 521202, India – sequence: 3 givenname: Kiran orcidid: 0000-0001-6925-1957 surname: Teeparthi fullname: Teeparthi, Kiran email: kiran.t39@nitandhra.ac.in organization: Department of Electrical Engineering, National Institute of Technology Andhra Pradesh, Tadepalligudem, 534101, India |
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| Cites_doi | 10.1016/j.ijepes.2023.109464 10.1109/TII.2019.2921106 10.1109/TCNS.2019.2901714 10.1016/j.ijepes.2022.108612 10.1016/j.ijepes.2020.106441 10.1109/TPWRS.2021.3076671 10.1016/j.cose.2020.101994 10.1049/iet-cps.2017.0013 10.1109/TSG.2020.3010510 10.1109/TAI.2023.3286831 10.1016/j.epsr.2024.111149 10.1016/j.neucom.2016.12.109 10.1016/j.neucom.2020.06.001 10.3390/en12112209 10.1109/TSG.2017.2703842 10.1016/j.automatica.2023.111100 10.1049/iet-gtd.2018.6195 10.1109/TII.2018.2825243 10.1016/j.energy.2022.125865 10.1109/TCNS.2014.2357531 10.1109/TSG.2019.2895306 10.1016/j.epsr.2015.12.029 10.1016/j.cie.2021.107864 10.1109/TSG.2017.2675960 10.1109/NAPS58826.2023.10318579 10.1109/ICCV.2017.324 10.1109/TSG.2017.2758600 10.1016/j.neucom.2018.09.094 10.1109/TSG.2018.2813280 10.1016/j.apenergy.2022.118828 10.1109/ICASSP.2019.8683634 10.1109/TSG.2019.2933006 10.1109/TPWRS.2017.2779129 10.1109/TSG.2017.2680542 10.1016/j.measurement.2022.111259 10.1109/TSG.2021.3109628 10.1109/JIOT.2020.2983911 10.1109/ACCESS.2018.2856520 |
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| Keywords | Distribution system state estimation Graph-based temporal convolutional network Denoising autoencoder Line current sensors False data injection attacks Topology identification |
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