State Estimation in Smart Distribution System With Low-Precision Measurements
For smart grid operators, this work addresses the practical challenge of handling large-scale, heterogeneous measurement data with limited communication bandwidth.
The paper proposes a state estimation method for smart distribution grids that uses low-precision measurements to reduce data volume, and a Bayesian belief inference framework to integrate measurements of varying quality. Simulations show it significantly outperforms other linear estimators.
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.