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

Author

Listed:
  • 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)

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.

Suggested Citation

  • Sepideh Radhoush & Trevor Vannoy & Kaveen Liyanage & Bradley M. Whitaker & Hashem Nehrir, 2023. "Distribution System State Estimation and False Data Injection Attack Detection with a Multi-Output Deep Neural Network," Energies, MDPI, vol. 16(5), pages 1-22, February.
    • Handle: RePEc:gam:jeners:v:16:y:2023:i:5:p:2288-:d:1082085
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    References listed on IDEAS

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    1. Ahmad, Fiaz & Rasool, Akhtar & Ozsoy, Emre & Sekar, Raja & Sabanovic, Asif & Elitaş, Meltem, 2018. "Distribution system state estimation-A step towards smart grid," Renewable and Sustainable Energy Reviews, Elsevier, vol. 81(P2), pages 2659-2671.
    2. Kyung-Yong Lee & Jung-Sung Park & Yun-Su Kim, 2021. "Optimal Placement of PMU to Enhance Supervised Learning-Based Pseudo-Measurement Modelling Accuracy in Distribution Network," Energies, MDPI, vol. 14(22), pages 1-18, November.
    3. Mansouri, Seyed Amir & Rezaee Jordehi, Ahmad & Marzband, Mousa & Tostado-Véliz, Marcos & Jurado, Francisco & Aguado, José A., 2023. "An IoT-enabled hierarchical decentralized framework for multi-energy microgrids market management in the presence of smart prosumers using a deep learning-based forecaster," Applied Energy, Elsevier, vol. 333(C).
    4. Antonio E. Saldaña-González & Andreas Sumper & Mònica Aragüés-Peñalba & Miha Smolnikar, 2020. "Advanced Distribution Measurement Technologies and Data Applications for Smart Grids: A Review," Energies, MDPI, vol. 13(14), pages 1-34, July.
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    Cited by:

    1. Guoqing Zhang & Wengen Gao & Yunfei Li & Xinxin Guo & Pengfei Hu & Jiaming Zhu, 2023. "Detection of False Data Injection Attacks in a Smart Grid Based on WLS and an Adaptive Interpolation Extended Kalman Filter," Energies, MDPI, vol. 16(20), pages 1-20, October.
    2. Muhammad Awais Shahid & Fiaz Ahmad & Rehan Nawaz & Saad Ullah Khan & Abdul Wadood & Hani Albalawi, 2023. "A Novel False Measurement Data Detection Mechanism for Smart Grids," Energies, MDPI, vol. 16(18), pages 1-17, September.
    3. Murilo Eduardo Casteroba Bento, 2024. "Load Margin Assessment of Power Systems Using Physics-Informed Neural Network with Optimized Parameters," Energies, MDPI, vol. 17(7), pages 1-20, March.

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