LAUSR

To monitor the power grid over a wide area, the wide area monitoring systems (WAMSs) has been developed. At each substation, the Global Positioning System (GPS) receiving system resides to provide trusted timing. Thus, it is critical for the WAMS to maintain authentic GPS timing over a wide area. However, GPS timing is susceptible to spoofing due to the unencrypted signal structure and its low signal power. Thus, to obtain trusted GPS timing from spoofing, a new wide-area monitoring algorithm, which comprises distributed belief propagation (BP) and a bidirectional recurrent neural network (RNN), is developed under the framework of artificial intelligence (AI). This joint BP-RNN algorithm authenticates each power substation by evaluating the estimated GPS timing error by its distributed processing capability. Specifically, the bidirectional RNN provides fast timing error estimation under the framework of AI. Simulation results demonstrate a fast detection time over the Kullback-Leibler divergence-based approach, and timing error estimation accuracy over the limit provided by the IEEE C37.118.1-2011 standard.