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Filters: Author is Davis, Katherine  [Clear All Filters]
2020-08-24
Huang, Hao, Kazerooni, Maryam, Hossain-McKenzie, Shamina, Etigowni, Sriharsha, Zonouz, Saman, Davis, Katherine.  2019.  Fast Generation Redispatch Techniques for Automated Remedial Action Schemes. 2019 20th International Conference on Intelligent System Application to Power Systems (ISAP). :1–8.
To ensure power system operational security, it not only requires security incident detection, but also automated intrusion response and recovery mechanisms to tolerate failures and maintain the system's functionalities. In this paper, we present a design procedure for remedial action schemes (RAS) that improves the power systems resiliency against accidental failures or malicious endeavors such as cyber attacks. A resilience-oriented optimal power flow is proposed, which optimizes the system security instead of the generation cost. To improve its speed for online application, a fast greedy algorithm is presented to narrow the search space. The proposed techniques are computationally efficient and are suitable for online RAS applications in large-scale power systems. To demonstrate the effectiveness of the proposed methods, there are two case studies with IEEE 24-bus and IEEE 118-bus systems.
2020-03-02
Sahu, Abhijeet, Huang, Hao, Davis, Katherine, Zonouz, Saman.  2019.  SCORE: A Security-Oriented Cyber-Physical Optimal Response Engine. 2019 IEEE International Conference on Communications, Control, and Computing Technologies for Smart Grids (SmartGridComm). :1–6.

Automatic optimal response systems are essential for preserving power system resilience and ensuring faster recovery from emergency under cyber compromise. Numerous research works have developed such response engine for cyber and physical system recovery separately. In this paper, we propose a novel cyber-physical decision support system, SCORE, that computes optimal actions considering pure and hybrid cyber-physical states, using Markov Decision Process (MDP). Such an automatic decision making engine can assist power system operators and network administrators to make a faster response to prevent cascading failures and attack escalation respectively. The hybrid nature of the engine makes the reward and state transition model of the MDP unique. Value iteration and policy iteration techniques are used to compute the optimal actions. Tests are performed on three and five substation power systems to recover from attacks that compromise relays to cause transmission line overflow. The paper also analyses the impact of reward and state transition model on computation. Corresponding results verify the efficacy of the proposed engine.