Detection of Bad Data in Multi-area State Estimation Conference Paper

abstract

• 2017 IEEE. This paper proposed an algorithm to quickly detect whether a sub-area contains bad data in multi-area state estimation. Each area in the multi-area state estimation will compute a sensitivity index that is based on the weight of local measurement residual and the overall change of measurement residual. The area with the highest index is candidate for locating bad data. This algorithm is also extendable to detect false data injection in a control area while traditional Chi-squared test may be rendered less effective. The proposed algorithm can help Central Control Center to locate the bad data in a multi-area system and can also prevent the system from potential false data injection attacks. Numerical studies based on the IEEE 14-bus system suggest the validity and efficacy of the proposed algorithm.

name of conference

• 2017 IEEE Texas Power and Energy Conference (TPEC)

authors

• Xie, Le

published proceedings

• 2017 IEEE TEXAS POWER AND ENERGY CONFERENCE (TPEC)

author list (cited authors)

• Zhou, Y., & Xie, L. e.

citation count

• 3

complete list of authors

• Zhou, Yuqi||Xie, Le

publication date

• February 2017

publisher

• Institute of Electrical and Electronics Engineers (IEEE)  Publisher

keywords

• Bad Data Detection
• False Data Injection Attacks
• Multi-area State Estimation

Digital Object Identifier (DOI)

• 10.1109/tpec.2017.7868276

International Standard Book Number (ISBN) 13

• 9781509066179

start page

• 1

end page

• 6