Abstract:

To monitor the power grid over a wide-area, the wide-area monitoring system (WAMS) has been developed. At each substation, the Global Positioning System (GPS) receiving system resides to provide a trusted timing. Thus, it is critical for the WAMS to maintain an authentic GPS timing over a widearea. However, the GPS timing is susceptible to spoofing due to the unencrypted signal structure and its low signal power. Thus, to obtain the trusted GPS timing from spoofing, a new wide-area monitoring algorithm, which is comprised of distributed belief propagation (BP) and a bi-directional recurrent neural network (RNN), is developed under the frame 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. Especially, the bi-directional RNN provides a fast timing error estimation under the frame of AI. Simulation results validate the fast detection time over the Kullback–Leibler divergencebased approach, and timing error estimation accuracy over the limit provided by the IEEE C37.118.1-2011 standard.