Multi-Area Distribution System State Estimation via Distributed Tensor Completion

TL;DR

This paper introduces a distributed, tensor-based approach for distribution system state estimation that leverages local measurements and tensor completion techniques, enabling efficient and scalable network state recovery.

Contribution

It presents a novel distributed tensor completion method for multi-area distribution system state estimation using canonical polyadic decomposition and ADMM.

Findings

The proposed algorithm accurately estimates network states in simulations.

Convergence and identifiability conditions are theoretically established.

Numerical tests on IEEE 123-bus system validate effectiveness.

Abstract

This paper proposes a model-free distribution system state estimation method based on tensor completion using canonical polyadic decomposition. In particular, we consider a setting where the network is divided into multiple areas. The measured physical quantities at buses located in the same area are processed by an area controller. A three-way tensor is constructed to collect these measured quantities. The measurements are analyzed locally to recover the full state information of the network. A distributed closed-form iterative algorithm based on the alternating direction method of multipliers is developed to obtain the low-rank factors of the whole network state tensor where information exchange happens only between neighboring areas. The convergence properties of the distributed algorithm and the sufficient conditions on the number of samples for each smaller network that guarantee the identifiability of the factors of the state tensor are presented. To demonstrate the efficacy of the proposed algorithm and to check the identifiability conditions, numerical simulations are carried out using the IEEE 123-bus system.