Detection of Bad Data in Multi-Area State Estimation Conference Paper uri icon

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.

author list (cited authors)

  • Zhou, Y., & Xie, L. e.

citation count

  • 2

publication date

  • February 2017

publisher