Abstract
Phase measurement unit (PMU) is an important part of the smart grid that collects accurate grid data simultaneously. However, the presence of GPS spoofing attack (GSA) can cause bias in the measurement data collected by the PMU and affects the normal operation of the PMU. In addition, existing schemes have difficulty in detecting slow continuous GSAs that are highly concealed. To address the above problems, in this paper, we propose a GSA detection scheme based on adaptive generalized cumulative. PMU data are collected via an adaptive sliding window. The state of the proposed system model is estimated based on an improved Kalman filter. The probability of attack occurrence is accumulated according to the generalized cumulative, so that small slow continuous type of attacks can be detected and different types of attacks can be distinguished. The effectiveness of the detection scheme is verified via simulation of mutant, slow continuous, and hybrid GSAs on an IEEE-39 node network.
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