Distribution System State Estimation and False Data Injection Attack Detection with a Multi-Output Deep Neural Network

Sepideh Radhoush, Trevor Vannoy, Kaveen Liyanage, Bradley M. Whitaker and Hashem Nehrir ()
Additional contact information
Sepideh Radhoush: Electrical and Computer engineering Department, Montana State University, Bozeman, MT 59717, USA
Trevor Vannoy: Electrical and Computer engineering Department, Montana State University, Bozeman, MT 59717, USA
Kaveen Liyanage: Electrical and Computer engineering Department, Montana State University, Bozeman, MT 59717, USA
Bradley M. Whitaker: Electrical and Computer engineering Department, Montana State University, Bozeman, MT 59717, USA
Hashem Nehrir: Electrical and Computer engineering Department, Montana State University, Bozeman, MT 59717, USA

Energies, 2023, vol. 16, issue 5, 1-22

Abstract: Distribution system state estimation (DSSE) has been introduced to monitor distribution grids; however, due to the incorporation of distributed generations (DGs), traditional DSSE methods are not able to reveal the operational conditions of active distribution networks (ADNs). DSSE calculation depends heavily on real measurements from measurement devices in distribution networks. However, the accuracy of real measurements and DSSE results can be significantly affected by false data injection attacks (FDIAs). Conventional FDIA detection techniques are often unable to identify FDIAs into measurement data. In this study, a novel deep neural network approach is proposed to simultaneously perform DSSE calculation (i.e., regression) and FDIA detection (i.e., binary classification) using real measurements. In the proposed work, the classification nodes in the DNN allow us to identify which measurements on which phasor measurement unit (PMU), if any, were affected. In the proposed approach, we aim to show that the proposed method can perform DSSE calculation and identify FDIAs from the available measurements simultaneously with high accuracy. We compare our proposed method to the traditional approach of detecting FDIAs and performing SE calculations separately; moreover, DSSE results are compared with the weighted least square (WLS) algorithm, which is a common model-based method. The proposed method achieves better DSSE performance than the WLS method and the separate DSSE/FDIA method in presence of erroneous measurements; our method also executes faster than the other methods. The effectiveness of the proposed method is validated using two FDIA schemes in two case studies: one using a modified IEEE 33-bus distribution system without DGs, and the other using a modified IEEE 69-bus system with DGs. The results illustrated that the accuracy and F 1-score of the proposed method are better than when performing binary classification only. The proposed method successfully detected the FDIAs on each PMU measurement. Moreover, the results of DSSE calculation from the proposed method has a better performance compared to the regression-only method, and the WLS methods in the presence of bad data.

Keywords: distribution system state estimation; false data injection attacks; deep neural network; weighted least square; active distribution network; bad data detection (search for similar items in EconPapers)
JEL-codes: Q Q0 Q4 Q40 Q41 Q42 Q43 Q47 Q48 Q49 (search for similar items in EconPapers)
Date: 2023
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (4)

Downloads: (external link)
https://www.mdpi.com/1996-1073/16/5/2288/pdf (application/pdf)
https://www.mdpi.com/1996-1073/16/5/2288/ (text/html)

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:gam:jeners:v:16:y:2023:i:5:p:2288-:d:1082085

Access Statistics for this article

Energies is currently edited by Ms. Agatha Cao

More articles in Energies from MDPI
Bibliographic data for series maintained by MDPI Indexing Manager ().