Online bad data detection using kernel density estimation

This paper addresses the problem of bad data detection in the power grid. An online probability density based technique is presented to identify bad measurements within a sensor data stream in a decentralized manner using only the data from the neighboring buses and a one-hop communication system. Analyzing the spatial and temporal dependency between the measurements, the proposed algorithm identifies the bad data. The algorithm was then tested on the IEEE 14-bus test system where it demonstrated superior performance detecting critical and multiple bad data compared to the largest normalized residual test.