  Efficient and accurate state estimation is essential for the optimal
management of the future smart grid. However, to meet the requirements of
deploying the future grid at a large scale, the state estimation algorithm must
be able to accomplish two major tasks: (1) combining measurement data with
different qualities to attain an optimal state estimate and (2) dealing with
the large number of measurement data rendered by meter devices. To address
these two tasks, we first propose a practical solution using a very short word
length to represent a partial measurement of the system state in the meter
device to reduce the amount of data. We then develop a unified probabilistic
framework based on a Bayesian belief inference to incorporate measurements of
different qualities to obtain an optimal state estimate. Simulation results
demonstrate that the proposed scheme significantly outperforms other linear
estimators in different test scenarios. These findings indicate that the
proposed scheme not only has the ability to integrate data with different
qualities but can also decrease the amount of data that needs to be transmitted
and processed.
