Title :
Online bad data detection using kernel density estimation
Author :
Muhammad Sharif Uddin;Anthony Kuh; Yang Weng;Marija Ilić
Author_Institution :
Dept. of Electrical Engineering, University of Hawaii at Manoa, Honolulu, USA
fDate :
7/1/2015 12:00:00 AM
Abstract :
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.
Keywords :
"Kernel","Clustering algorithms","Estimation","Bandwidth","Power measurement","Power grids"
Conference_Titel :
Power & Energy Society General Meeting, 2015 IEEE
DOI :
10.1109/PESGM.2015.7286013