Identification of topology changes in power grids using phasor measurements
Scott Vander Wiel,
Russell Bent,
Emily Casleton and
Earl Lawrence
Applied Stochastic Models in Business and Industry, 2014, vol. 30, issue 6, 740-752
Abstract:
Phasor measurement units (PMUs) are increasingly important for monitoring the state of an electrical power grid and quickly detecting topology changes caused by events such as lines going down or large loads being dropped. Phasors are complex‐valued measurements of voltage and current at various points of generation and consumption. If a line goes down or a load is removed, power flows change throughout the grid according to known physical laws, and the probability distribution of phasor measurements changes accordingly. This paper develops a method to estimate the current topology of a power grid from phasor measurements and considers the design goal of placing PMUs at strategic points in a distribution system to achieve good sensitivity to single‐line outages. From a vector of phasor measurements, probabilities are computed corresponding to the scenario that all power lines are operational and to alternate scenarios in which each line goes down individually. These probabilities are functions of the joint distributions of phasor measurements under each possible scenario, obtained through Monte Carlo simulations with random load profiles. We use log‐spline densities to estimate marginal distributions of phasor measurements and fold these into a multivariate Gaussian copula to capture important correlations. Sensitivity to outages varies according to which line goes down and where PMUs are placed on the grid. A greedy search algorithm is demonstrated for placing PMUs at locations that provide good sensitivity to single‐line outages. Published 2014. This article is a U.S. Government work and is in the public domain in the USA.
Date: 2014
References: Add references at CitEc
Citations:
Downloads: (external link)
https://doi.org/10.1002/asmb.2082
Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.
Export reference: BibTeX
RIS (EndNote, ProCite, RefMan)
HTML/Text
Persistent link: https://EconPapers.repec.org/RePEc:wly:apsmbi:v:30:y:2014:i:6:p:740-752
Access Statistics for this article
More articles in Applied Stochastic Models in Business and Industry from John Wiley & Sons
Bibliographic data for series maintained by Wiley Content Delivery ().