On state estimation with bad data detection

Weiyu Xu, Meng Wang, Ao Tang
2011 IEEE Conference on Decision and Control and European Control Conference  
We consider the problem of state estimation through observations corrupted by both bad data and additive observation noises. A mixed 1 and 2 convex programming is used to separate both sparse bad data and additive noises from the observations. Using the almost Euclidean property for a linear subspace, we derive a new performance bound for the state estimation error under sparse bad data and additive observation noises. Our main contribution is to provide sharp bounds on the almost Euclidean
more » ... lmost Euclidean property of a linear subspace, using the "escape-through-a-mesh" theorem from geometric functional analysis. We also propose and numerically evaluate an iterative convex programming approach to solve bad data detection problems in electrical power networks.
doi:10.1109/cdc.2011.6161214 dblp:conf/cdc/XuWT11 fatcat:nvz3bi6qxvcfbbu3z7d7ojhwim