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Artificial-Intelligence-Based Distributed Belief Propagation and Recurrent Neural Network Algorithm for Wide-Area Monitoring Systems
IEEE NETWORK ( IF 9.3 ) Pub Date : 2020-06-02 , DOI: 10.1109/mnet.011.1900322
Sriramya Bhamidipati , Kyeong Jin Kim , Hongbo Sun , Philip V. Orlik

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.

中文翻译:

基于人工智能的广域监控系统分布式信念传播和递归神经网络算法

为了监视大范围的电网,已经开发了大范围监视系统(WAMS)。在每个变电站,全球定位系统(GPS)接收系统都位于提供可靠的时间安排。因此,对于WAMS而言,在大范围内保持可靠的GPS定时至关重要。但是,由于未加密的信号结构及其低信号功率,GPS定时容易受到欺骗的影响。因此,为了从欺骗中获得可信的GPS定时,在人工智能(AI)的框架下,开发了一种新的广域监视算法,该算法包括分布式信念传播(BP)和双向递归神经网络(RNN)。这种联合BP-RNN算法通过评估其分布式处理能力估算的GPS定时误差来认证每个变电站。特别,双向RNN在AI框架下提供快速的时序误差估计。仿真结果表明,与基于Kullback-Leibler散度的方法相比,该方法具有更快的检测时间,并且在IEEE C37.118.1-2011标准所提供的限制范围内,定时误差估计精度也很高。
更新日期:2020-06-02
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