  Distribution network topology detection and state estimation in real-time are
critical for modern distribution systems management and control. However,
number of sensors in distribution networks are limited and communication links
between switch devices and distribution management system are not
well-established. In this regard, this paper proposes mixed-integer quadratic
programming (MIQP) formulations to determine the topology of distribution
network and estimate distribution system states simultaneously using
micro-phasor measurement units (micro-PMUs) and smart meter data. Two
approaches based on AC optimal power flow are proposed and analyzed: (i) polar
power-voltage (PPV) formulation, and (ii) rectangular current-voltage (RIV)
formulation. The proposed models include convex objective function while
constraints are linearized using first-order approximation of Taylor series and
Big M method. The proposed models can identify multiple simultaneous switching
actions at each time instant and different topology configurations including
radial and meshed networks. Only measurement data at each time interval is
needed to identify topology and states of the system correctly. The proposed
models are tested on a modified IEEE-33 bus system with realistic load data
from Pecan Street Inc. database. The results confirm that both models can
identify system topology and states with remarkable accuracy in real-time,
while RIV model outperforms PPV model.
