Abstract
Due to the integration of wireless technology in power systems, the issue of cybersecurity becomes very critical. Cyberattacks mainly cause changes in power system measurement data like GPS spoofing attack (GSA), which modifies the phase angle of Phasor Measurement Units (PMUs) data. The GSA may lead to disruption in monitoring and protection systems. In this paper, a neural network GPS spoofing detection (NNGSD) with employing PMU data from the dynamic power system is proposed to detect GSAs. Due to the use of an artificial intelligence-based kernel, the proposed structure can be used in various power grid conditions, such as load changes in the grid, in the presence of noise and multi-GSA on PMUs. NNGSD is applied to the standard IEEE 14-buses power system. Numerical results show the real-time performance of the proposed detection method in different conditions.
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