Learning Exact Topology of a Loopy Power Grid from Ambient Dynamics

TL;DR

This paper introduces a new method to accurately determine the topology of complex power grids using ambient voltage measurements and multivariate Wiener filtering, enhancing grid reliability and operation.

Contribution

It presents a novel framework for topology estimation in general power grids using time-series voltage data and Wiener filtering, applicable to loopy and radial networks.

Findings

Successfully estimates grid topology from simulated data.

Works on standard IEEE test cases.

Effective for both loopy and radial grids.

Abstract

Estimation of the operational topology of the power grid is necessary for optimal market settlement and reliable dynamic operation of the grid. This paper presents a novel framework for topology estimation for general power grids (loopy or radial) using time-series measurements of nodal voltage phase angles that arise from the swing dynamics. Our learning framework utilizes multivariate Wiener filtering to unravel the interaction between fluctuations in voltage angles at different nodes and identifies operational edges by considering the phase response of the elements of the multivariate Wiener filter. The performance of our learning framework is demonstrated through simulations on standard IEEE test cases.

Full text

See pages 1-last of ACM_2017_final.pdf