Identification of outages in power systems with uncertain states and optimal sensor locations

Yue Zhao, Jianshu Chen, Andrea Goldsmith, H. Vincent Poor

Research output: Contribution to journalArticlepeer-review

44 Scopus citations


Joint outage identification and state estimation in power systems is studied. A Bayesian framework is employed, and a Gaussian prior distribution of the states is assumed. The joint posterior of the outage hypotheses and the network states is developed in closed form, which can be applied to obtain the optimal joint detector and estimator under any given performance criterion. Employing the minimum probability of error as the performance criterion in identifying outages with uncertain states, the optimal detector is obtained. Efficiently computable performance metrics that capture the probability of error of the optimal detector are developed. Under simplified model assumptions, closed-form expressions for these metrics are derived, and these lead to a mixed integer convex programming problem for optimizing sensor locations. Using convex relaxations, a branch and bound algorithm that finds the globally optimal sensor locations is developed. Significant performance gains from using the optimal detector with the optimal sensor locations are observed from simulations. Furthermore, performance with greedily selected sensor locations is shown to be very close to that with globally optimal sensor locations.

Original languageEnglish (US)
Article number6862865
Pages (from-to)1140-1153
Number of pages14
JournalIEEE Journal on Selected Topics in Signal Processing
Issue number6
StatePublished - Dec 1 2014

All Science Journal Classification (ASJC) codes

  • Signal Processing
  • Electrical and Electronic Engineering


  • Chernoff bound
  • Joint detection and estimation
  • PMU
  • branch and bound
  • mixed integer convex programming
  • outage identification
  • sensor placement
  • smart grid
  • state estimation


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