A Convex Method of Generalized State Estimation using Circuit-theoretic Node-breaker Model

TL;DR

This paper introduces ckt-GSE, a convex, scalable, and robust circuit-theoretic method for generalized state estimation in power grids that simultaneously estimates states and detects topology errors amidst data inaccuracies.

Contribution

It develops the first convex, circuit-based AC network state estimation method that jointly estimates states and topology, robust against data errors and applicable to real-world scenarios.

Findings

Successfully estimates AC states and detects topology errors.

Robust against various data errors using WLAV objective.

Scalable linear programming solution for practical implementation.

Abstract

An accurate and up-to-date topology is critical for situational awareness of a power grid; however, wrong switch statuses due to physical damage, communication error, or cyber-attack, can often result in topology errors. To maintain situation awareness under the possible topology errors and bad data, this paper develops ckt-GSE, a circuit-theoretic generalized state estimation method using node-breaker (NB) model. Ckt- GSE is a convex and scalable model that jointly estimates AC state variables and network topology, with robustness against different data errors. The method first constructs an equivalent circuit representation of the AC power grid by developing and aggregating linear circuit models of SCADA meters, phasor measurement units(PMUs), and switching devices. Then based on this circuit, ckt-GSE defines a constrained optimization problem using weighted least absolute value (WLAV) objective to form a robust estimator. The problem is a Linear Programming (LP) problem whose solution includes accurate AC states and a sparse vector of noise terms to identify topology errors and bad data.This paper is the first to explore a circuit-theoretic approach for an AC-network constrained GSE algorithm that is: 1) applicable to the real-world data setting, 2) convex without relaxation, scalable with our circuit-based solver; and 3) robust with the ability to identify and reject different data errors