  This paper proposes an efficient algorithmic approach that overcomes the
critical challenges in the real-time unbalanced distribution system state
estimation, topology error processing, and outage identification
simultaneously: (1) Limited locations of measurement devices and unsynchronized
measurement data as well as missing and bad data, (2) Complicated mixed-phase
switch actions and mutual impedances, and (3) the nonlinear nature of
unbalanced distribution system power flow and measurement data . A single
snap-shot mixed-integer quadratic programming (MIQP) optimization framework is
proposed to cope with these challenges. This MIQP framework presents a more
accurate unbalanced distribution system model, linearizes the nonlinear
relationship between bus voltage and current injections, formulates the
complicated mixed-phase switch operations, and executes the outage detection
function via analytic constraints. The proposed model can be effectively solved
by a general commercial MIP solver. The effectiveness of the proposed approach
is verified on an actual distribution feeder in Arizona.
