  This paper introduces a stochastic hybrid system (SHS) framework in state
space model to capture sensor, communication, and system contingencies in
modern power systems (MPS). Within this new framework, the paper concentrates
on the development of state estimation methods and algorithms to provide
reliable state estimation under randomly intermittent and noisy sensor data.
MPSs employ diversified measurement devices for monitoring system operations
that are subject to random measurement errors and rely on communication
networks to transmit data whose channels encounter random packet loss and
interruptions. The contingency and noise form two distinct and interacting
stochastic processes that have a significant impact on state estimation
accuracy and reliability. This paper formulates stochastic hybrid system models
for MPSs, introduces coordinated observer design algorithms for state
estimation, and establishes their convergence and reliability properties. A
further study reveals a fundamental design tradeoff between convergence rates
and steady-state error variances. Simulation studies on the IEEE 5-bus system
and IEEE 33-bus system are used to illustrate the modeling methods, observer
design algorithms, convergence properties, performance evaluations, and impact
sensor system selections.
