Distributed State Estimation Using RSC Coded Smart Grid Communications

Abstract

Recently, the renewable distributed energy resources (DERs) have become more and more popular due to carbonfree energy sources and environment-friendly electricity generation. Unfortunately, these power generation patterns are mostly intermittent in nature and distributed over the electrical grid which creates challenging problems in the reliability of the smart grid. Thus, smart grid has a strong requisite for an efficient communication infrastructure to facilitate estimating the DER states. In contrast to the traditional methods of centralised state estimation, we propose a distributed approach to microgrid state estimation based on the concatenated coding structure. In this framework, the DER state is treated as a dynamic outer code and the recursive systematic convolutional (RSC) code is seen as a concatenated inner code for protection and redundancy in the system states. Furthermore, in order to properly monitor the intermittent energy source from any place, this paper proposes a distributed state estimation method. Particularly, the outputs of the local state estimation are treated as measurements which are fed into the master fusion station. At the end, the global state estimation can be obtained by combining local state estimations with corresponding weighting factors. The weighting factors can be calculated by inspiring the covariance intersection method. The simulation results show that the proposed method is able to estimate the system state properly.

Microgrid State Estimation and IoT: Modelling Concepts and Design Algorithm

“…, x(t n )}. In order to formulate the convex optimization problem, a convex region for the possible range of the reconstructed signal is defined according to (16):…”

Distributed State Estimation of Smart Grids with Packet Losses

“…By substituting (18) into (19), one can obtain the following estimation error covariance matrix expression:…”

“…However, it considers an unreliable communication channel and is only suitable for the centralized power system state estimation and control. A decentralized dynamic power system state estimation without packet losses framework is proposed in . In order to reduce the communication burden, especially in the island wind farms and grid across mountain areas, an MMSE‐based distributed state estimation is recommended in .…”

Distributed State Estimation of Multi-region Power System based on Consensus Theory

“…The distributed state estimation models with proper solution approaches have been investigated by some scholars [2][3][4][5]. A distributed state estimation model was established with a hierarchical 2 of 16 framework in [6], where the local estimation results of the first layer need to be globally coordinated at the center of the second layer.…”