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Most System Integrity Protection (SIP) schemes deployed in smart gird today are centralized functions relying on wide-area communication. The highly centralized implementation makes SIP susceptible to the single point of failure induced by cyber attacks. In this paper, we present a novel multi-agent-based design to enhance the cyber resilience of SIP while focusing on augmenting its situational awareness and self-adaptiveness. Specifically, we have investigated data-driven anomaly detection and adaptive load rejection within the decentralized SIP set-up. After attaining a comprehensive taxonomy of operation states of a power grid as a cyber-physical system, we are able to convert the anomaly detection to a multi-class classification problem. A supervised learning algorithm, named as Support Vector Machine embedded Layered Decision Tree (SVMLDT), is proposed as a possible solution. Anomaly detection is carried out by every agent separately, but the final decision depends on the consensus among all interconnected agents. Besides, we propose an adaptive load rejection strategy to mitigate the Denial of Service (DoS) attacks targeting the load shedding scheme. A real load rejection SIP scheme adopted by Salt River Project is modified to fit in the IEEE 39-bus model as a study case. Experiment results show that the proposed SIP can detect anomalous grid operation states and then adjust its remedial actions accordingly to adapt to the under-attack situations.
Wang et al. (Fri,) studied this question.
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