State Estimation in Smart Distribution System With Low-Precision Measurements

TL;DR

This paper presents a Bayesian-based state estimation method for smart distribution systems that effectively integrates low-precision measurements, reducing data transmission and processing while improving estimation accuracy.

Contribution

It introduces a practical low-precision measurement representation and a unified probabilistic framework for optimal state estimation in large-scale smart grids.

Findings

Outperforms linear estimators in various scenarios

Effectively integrates measurements of different qualities

Reduces data transmission and processing requirements

Abstract

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.