A smart grid requires the implementation of ICT technologies in order to incorporate new functions into electricity grid monitoring and control. Wide Area Monitoring Systems (WAMSs) are used to measure synchrophasor data at different locations and give operators a near-real-time picture of what is happening in the system. The measurement data is periodically collected via communication channels to monitor, predict and control the power consumption, and detect any problems in the power grid. Attacks on WAMSs can trigger wrong decisions and create dangerous failures in the smart grid system. In this paper, we investigate data integrity attacks at different attack entry points of a WAMS, their impacts on the smart grid system, and existing mitigation strategies. We conclude from our study that the existing techniques, methodologies and mechanisms are not effective enough to detect or mitigate some attacks.

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Clustering analysis is a hot research in the field of complex network, in order to overcome high time complexity, difficulty for the user to select initial conditions and other defects of the existing clustering algorithms, this paper analyses the above problems and proposes an adaptive clustering algorithm based on data field in complex networks. First, the importance factor is proposed to dig out the important vertices in networks as the center of the cluster which is based on the defects and merits of evaluation indexes of the vertex's degree, mutual information and closeness respectively. Due to the vertices in networks connected and react upon one another, the theory of data field in physics was introduced into complex networks, by calculating field-strength and potential function of vertices to realize clustering of vertices-cluster topology structure division. Simulation experiments show that the adaptive algorithm can get approximate optical cluster topology structures with a low time complexity, and has a higher accuracy and validity compared to other algorithms.