Robust Power System Stability Assessment Against Adversarial Machine Learning-Based Cyberattacks via Online Purification

Zhao, Tianqiao; Yue, Meng; Wang, Jianhui

doi:10.1109/tpwrs.2022.3233735

Robust Power System Stability Assessment Against Adversarial Machine Learning-Based Cyberattacks via Online Purification

Journal Article · Mon Jan 02 00:00:00 EST 2023 · IEEE Transactions on Power Systems

DOI:https://doi.org/10.1109/tpwrs.2022.3233735· OSTI ID:2440518

^[1]; ^[1]; ^[2]

Brookhaven National Laboratory (BNL), Upton, NY (United States)
Southern Methodist Univ., Dallas, TX (United States)

The increasing complexity associated with renewable generation brings more challenges to power system stability assessment (SA). Data-driven approaches based on machine learning (ML) techniques for stability assessment have received significant research interest and shown their promising performance. However, ML-based models are recognized to be vulnerable to adversarial disturbances, where a slight perturbation to power system measurements could lead to unacceptable errors. To address this issue, this paper develops a novel lightweight mitigation strategy, i.e., robust online stability assessment (ROSA), to enhance the ML-based assessment model against both white-box and the black-box adversarial disturbances (i.e., purification) in the online implementation. The ROSA involves a supervised learning-based module for the primary stability assessment and a self-supervised learning-based module. Further, the two modules are trained jointly with different objective (loss) functions and implemented in sequence. A suitable purification objective and various time-series data augmentation methods are designed for SA applications to tackle adversarial disturbances adaptively. Case studies are performed, and the comparative results have clearly illustrated the competitive, robust accuracy against various adversarial scenarios and verified the effectiveness of the proposed online purification strategy.

View Accepted Manuscript (DOE)

Research Organization:: Brookhaven National Laboratory (BNL), Upton, NY (United States)

Sponsoring Organization:: USDOE Office of Electricity (OE), Advanced Grid Research & Development. Power Systems Engineering Research

Grant/Contract Number:: SC0012704

OSTI ID:: 2440518

Report Number(s):: BNL-226092-2024-JAAM

Journal Information:: IEEE Transactions on Power Systems, Vol. 38, Issue 6; ISSN 0885-8950

Publisher:: IEEECopyright Statement

Country of Publication:: United States

Language:: English

References (23)

Improving Confidence Estimates for Unfamiliar Examples Li, Zhizhong; Hoiem, Derek 2020 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) https://doi.org/10.1109/CVPR42600.2020.00276	conference	June 2020
A Self-supervised Approach for Adversarial Robustness Naseer, Muzammal; Khan, Salman; Hayat, Munawar 2020 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) https://doi.org/10.1109/CVPR42600.2020.00034	conference	June 2020
Adversarial Robustness: From Self-Supervised Pre-Training to Fine-Tuning Chen, Tianlong; Liu, Sijia; Chang, Shiyu 2020 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) https://doi.org/10.1109/CVPR42600.2020.00078	conference	June 2020
Comprehensive Review on Detection and Classification of Power Quality Disturbances in Utility Grid With Renewable Energy Penetration Chawda, Gajendra Singh; Shaik, Abdul Gafoor; Shaik, Mahmood IEEE Access, Vol. 8 https://doi.org/10.1109/ACCESS.2020.3014732	journal	January 2020
Hierarchical Deep Learning Machine for Power System Online Transient Stability Prediction Zhu, Lipeng; Hill, David J.; Lu, Chao IEEE Transactions on Power Systems, Vol. 35, Issue 3 https://doi.org/10.1109/TPWRS.2019.2957377	journal	May 2020
A Review of False Data Injection Attacks Against Modern Power Systems Liang, Gaoqi; Zhao, Junhua; Luo, Fengji IEEE Transactions on Smart Grid, Vol. 8, Issue 4 https://doi.org/10.1109/TSG.2015.2495133	journal	July 2017
Anomaly Detection and Mitigation for Wide-Area Damping Control using Machine Learning Ravikumar, Gelli; Govindarasu, Manimaran IEEE Transactions on Smart Grid https://doi.org/10.1109/TSG.2020.2995313	journal	January 2020
A Hierarchical Self-Adaptive Data-Analytics Method for Real-Time Power System Short-Term Voltage Stability Assessment Zhang, Yuchen; Xu, Yan; Dong, Zhao Yang IEEE Transactions on Industrial Informatics, Vol. 15, Issue 1 https://doi.org/10.1109/TII.2018.2829818	journal	January 2019
Grid Structural Characteristics as Validation Criteria for Synthetic Networks Birchfield, Adam B.; Xu, Ti; Gegner, Kathleen M. IEEE Transactions on Power Systems, Vol. 32, Issue 4 https://doi.org/10.1109/TPWRS.2016.2616385	journal	July 2017
Support Vector Machines for Transient Stability Analysis of Large-Scale Power Systems Moulin, L. S.; daSilva, A. P. A.; El-Sharkawi, M. A. IEEE Transactions on Power Systems, Vol. 19, Issue 2 https://doi.org/10.1109/TPWRS.2004.826018	journal	May 2004
A Fully Data-Driven Method Based on Generative Adversarial Networks for Power System Dynamic Security Assessment With Missing Data Ren, Chao; Xu, Yan IEEE Transactions on Power Systems, Vol. 34, Issue 6 https://doi.org/10.1109/TPWRS.2019.2922671	journal	November 2019
Towards Evaluating the Robustness of Neural Networks Carlini, Nicholas; Wagner, David 2017 IEEE Symposium on Security and Privacy (SP) https://doi.org/10.1109/SP.2017.49	conference	May 2017
Practical Black-Box Attacks against Machine Learning Papernot, Nicolas; McDaniel, Patrick; Goodfellow, Ian ASIA CCS '17: ACM Asia Conference on Computer and Communications Security, Proceedings of the 2017 ACM on Asia Conference on Computer and Communications Security https://doi.org/10.1145/3052973.3053009	conference	April 2017
Intelligent Time-Adaptive Transient Stability Assessment System Yu, James J. Q.; Hill, David J.; Lam, Albert Y. S. IEEE Transactions on Power Systems, Vol. 33, Issue 1 https://doi.org/10.1109/TPWRS.2017.2707501	journal	January 2018
A Unified Online Deep Learning Prediction Model for Small Signal and Transient Stability Azman, Syafiq Kamarul; Isbeih, Younes J.; Moursi, Mohamed Shawky El IEEE Transactions on Power Systems, Vol. 35, Issue 6 https://doi.org/10.1109/TPWRS.2020.2999102	journal	November 2020
ZOO: Zeroth Order Optimization Based Black-box Attacks to Deep Neural Networks without Training Substitute Models Chen, Pin-Yu; Zhang, Huan; Sharma, Yash CCS '17: 2017 ACM SIGSAC Conference on Computer and Communications Security, Proceedings of the 10th ACM Workshop on Artificial Intelligence and Security https://doi.org/10.1145/3128572.3140448	conference	November 2017
Transient stability assessment combined model framework based on cost‐sensitive method Wang, Huaiyuan; Chen, Qifan; Zhang, Baohui IET Generation, Transmission & Distribution, Vol. 14, Issue 12 https://doi.org/10.1049/iet-gtd.2019.1562	journal	April 2020
Recent Developments in Machine Learning for Energy Systems Reliability Management Duchesne, Laurine; Karangelos, Efthymios; Wehenkel, Louis Proceedings of the IEEE, Vol. 108, Issue 9 https://doi.org/10.1109/JPROC.2020.2988715	journal	September 2020
A Review of Machine Learning Approaches to Power System Security and Stability Alimi, Oyeniyi Akeem; Ouahada, Khmaies; Abu-Mahfouz, Adnan M. IEEE Access, Vol. 8 https://doi.org/10.1109/ACCESS.2020.3003568	journal	January 2020
Vulnerability Analysis, Robustness Verification, and Mitigation Strategy for Machine Learning-Based Power System Stability Assessment Model Under Adversarial Examples Ren, Chao; Du, Xiaoning; Xu, Yan IEEE Transactions on Smart Grid, Vol. 13, Issue 2 https://doi.org/10.1109/TSG.2021.3133604	journal	March 2022
Adversarial Examples: Attacks and Defenses for Deep Learning Yuan, Xiaoyong; He, Pan; Zhu, Qile IEEE Transactions on Neural Networks and Learning Systems, Vol. 30, Issue 9 https://doi.org/10.1109/TNNLS.2018.2886017	journal	September 2019
Bayesian State Estimation for Unobservable Distribution Systems via Deep Learning Mestav, Kursat Rasim; Luengo-Rozas, Jaime; Tong, Lang IEEE Transactions on Power Systems, Vol. 34, Issue 6 https://doi.org/10.1109/TPWRS.2019.2919157	journal	November 2019
A PMU-Based Data-Driven Approach for Classifying Power System Events Considering Cyberattacks Ma, Rui; Basumallik, Sagnik; Eftekharnejad, Sara IEEE Systems Journal, Vol. 14, Issue 3 https://doi.org/10.1109/JSYST.2019.2963546	journal	September 2020

Similar Records

Autonomous System Inference, Trojan, and Adversarial Reprogramming Attack and Defense (Final)

Technical Report · Fri Sep 01 00:00:00 EDT 2023 · OSTI ID:2331437

Spirito, Christopher M.; Lamb, Patience

Online Power System Event Detection via Bidirectional Generative Adversarial Networks

Journal Article · Fri Feb 25 00:00:00 EST 2022 · IEEE Transactions on Power Systems · OSTI ID:1867798

Cheng, Yuanbin; Yu, Nanpeng; Foggo, Brandon; +1 more

Enabling Staged Pressurized Oxy-Combustion: Improving Flexibility and Performance at Reduced Cost

Technical Report · Wed Jul 31 00:00:00 EDT 2019 · OSTI ID:1545657

Hume, Scott Alexander

Related Subjects

24 POWER TRANSMISSION AND DISTRIBUTION
power system stability
stability criteria
predictive models
machine learning
purification
phasor measurement units
self-supervised learning
cyberattack
adversarial disturbances
adversarial purification
stability assessment

Robust Power System Stability Assessment Against Adversarial Machine Learning-Based Cyberattacks via Online Purification

Citation Formats

References (23)

Similar Records

Related Subjects