Conference proceeding
On state estimation with bad data detection
2011 50th IEEE Conference on Decision and Control and European Control Conference, pp.5989-5994
12/2011
DOI: 10.1109/CDC.2011.6161214
Abstract
We consider the problem of state estimation through observations corrupted by both bad data and additive observation noises. A mixed ℓ 1 and ℓ 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 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.
Details
- Title: Subtitle
- On state estimation with bad data detection
- Creators
- Weiyu Xu - Cornell UniversityMeng Wang - Cornell UniversityAo Tang - Cornell University
- Resource Type
- Conference proceeding
- Publication Details
- 2011 50th IEEE Conference on Decision and Control and European Control Conference, pp.5989-5994
- Publisher
- IEEE
- DOI
- 10.1109/CDC.2011.6161214
- ISSN
- 0191-2216
- Language
- English
- Date published
- 12/2011
- Academic Unit
- Electrical and Computer Engineering
- Record Identifier
- 9984197181202771
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