  Distribution system state estimation (DSSE) is an essential tool for
operation of distribution networks, the results of which enables the operator
to have a thorough observation of the system. Thus, most distribution
management systems (DMS) include a single-phase state estimator. Due to
non-convexity of the SE problem, heuristic and Newton methods do not guarantee
the global solution. In contrast, SDP based SE is more promising to guarantee
the globally optimal solution since it represents and solves the problem in a
convex format. However, the observability of the power system is highly
vulnerable to the set of measurements while employing the SDP-based SE, which
is addressed in this report. An algorithm is proposed to generate additional
measurements using the measurement data already gathered. The SDP-based SE is
very sensitive to the level of noise in large power networks. Also, bad data
detection algorithms proposed for Newton methods do not work for the SDP-based
SE method due to larger number of state variables in SDP representation of
power network. In this report, an algorithm is proposed to generate additional
measurements using the measurement data already gathered in order to solve the
observability issue. A network separation algorithm is developed to solve the
entire problem for smaller sub-networks which include micro-PMUs to mitigate
the adverse effects of noise for huge networks. An algorithm based on
redundancy test is also developed for bad data detection. The algorithms are
tested on single phase and multiphase test systems. The algorithms are applied
EPRI Circuit 5 (2998-bus) test feeder to demonstrate the flexibility of the
algorithms developed.
