Bad data detection in the context of leverage point attacks in modern power networks
File(s)
Published version
Author(s)
Majumdar, A
Pal, BC
Type
Journal Article
Abstract
This paper demonstrates a concept to detect bad data in state estimation when the leverage measurements are tampered with gross error. The concept is based on separating leverage measurements from non-leverage measurements by a technique called diagnostic robust generalized potential (DRGP), which also takes care of the masking or swamping effect, if any. The methodology then detects the erroneous measurements from the generalized studentized residuals (GSR). The effectiveness of the method is validated with a small illustrative example, standard IEEE 14-bus and 123-bus unbalanced network models and compared with the existing methods. The method is demonstrated to be potentially very useful to detect attacks in smart power grid targeting leverage points in the system.
Date Issued
2018-05-01
Date Acceptance
2016-08-31
Citation
IEEE Transactions on Smart Grid, 2018, 9 (3), pp.2042-2054
ISSN
1949-3061
Publisher
IEEE
Start Page
2042
End Page
2054
Journal / Book Title
IEEE Transactions on Smart Grid
Volume
9
Issue
3
Copyright Statement
© 2016 The Author(s). This work is licensed under a Creative Commons Attribution 3.0 License. For more information, see http://creativecommons.org/licenses/by/3.0/.
License URL
Sponsor
Engineering & Physical Science Research Council (E
Grant Number
EP/K02227X/1
Subjects
Science & Technology
Technology
Engineering, Electrical & Electronic
Engineering
Distribution management system (DMS)
remote terminal unit (RTU)
state estimation (SE)
leverage measurements
bad data detection (BDD)
generalized studentized residuals (GSR)
diagnostic-robust generalized potentials (DRGP)
SYSTEM STATE ESTIMATION
DATA INJECTION ATTACKS
MULTIPLE INFLUENTIAL OBSERVATIONS
LINEAR-REGRESSION
IDENTIFICATION
PMUS
0906 Electrical And Electronic Engineering
0915 Interdisciplinary Engineering
Publication Status
Published
Date Publish Online
2016-10-17