Details

Autor(en) / Beteiligte

• Zhang, Zhenyong
• Yau, David K.Y.

Titel

CoRE: Constrained Robustness Evaluation of Machine Learning-Based Stability Assessment for Power Systems

Ist Teil von

• IEEE/CAA journal of automatica sinica, 2023-02, Vol.10 (2), p.557-559

Ort / Verlag

Piscataway: Chinese Association of Automation (CAA)

Erscheinungsjahr

2023

Link zum Volltext

• IEEE_Xplore

Quelle

IEEE Xplore

Beschreibungen/Notizen

• Dear Editor, Machine learning (ML) approaches have been widely employed to enable real-time ML-based stability assessment (MLSA) of large-scale automated electricity grids. However, the vulnerability of MLSA to malicious cyber-attacks may lead to wrong decisions in operating the physical grid if its resilience properties are not well understood before deployment. Unlike adversarial ML in prior domains such as image processing, specific constraints of power systems that the attacker must obey in constructing adversarial samples require new research on MLSA vulnerability analysis for power systems. In this letter, we propose a novel evaluation framework to analyze the robustness of MLSA against adversarial samples with key considerations for damage (i.e., the ability of the adversarial data to cause ML misclassification), bad data detection, physical consistency, and limited attacker's capacity to corrupt data. Extensive experiments are conducted to evaluate the robustness of MLSA under different settings.