Detection and Location of Bias Load Injection Attack in Smart Grid via Robust Adaptive Observer

Abstract

This article studies the detection and location of the bias load injection attack (BLIA) in smart grid. As one of typical false data injection attacks (FDIAs), the BLIA aims at destroying the vulnerable generator load. In particularly, the BLIA can bypass the traditional bad data detection techniques, by compromising the measurable sensor-data estimation. Because of this reason, the emergency of BLIA brings enormous threat to the security of smart grid. To address this problem, a detection and location framework against BLIA consisting of three steps is proposed. In the first step, we propose a topology structure-based subregion division algorithm to reduce the complexity of attack detection in the large-scale grid system. In the second step, taking the stealthy characteristics of the BLIA into account, a robust adaptive observer-based detection algorithm is proposed. Through of capabilities of observers, we can estimate the physical dynamics accurately. To detect the BLIA quickly and avoid missed alarm, we compute the adaptive threshold as a substitute for the precomputed threshold, by taking the model linearization error and external disturbance into account. In the third step, we propose a logical judgment matrix to address the sensor attack undetectability problem under structure vulnerability, based on the combinations of all observable sensors. Finally, the effectiveness of the proposed detection and location framework is illustrated, by using detailed case studies on the IEEE 55-bus smart grid system.