A prediction model for anomalies in smart grid with sensor network


A machine learning based model to monitor the smart grid for any suspicious activity or malicious attack is presented in this paper. The model is designed to detect and classify anomalies in the sensory data and is helpful in ensuring the security and stability of the smart grid. The model relies on the real time data collected using wireless sensor networks as an overlay network on the power distribution grid. The overlay network of wireless sensors /devices uses a cluster topology at each tower to collect local information about the tower, and is further augmented by the linear chain topology to connect each tower to the base station (usually at the substation). Preliminary results show that detection mechanism is promising and is able to detect the occurrence of any anomalous event that may cause threat to the smart grid. Copyright 2012 ACM.

Publication Title

ACM International Conference Proceeding Series