Convexification of bad data and topology error detection and identification problems in AC electric power systems

Yang Weng, Marija D. Ilić, Qiao Li, Rohit Negi

Research output: Contribution to journalArticle

17 Citations (Scopus)

Abstract

This study is motivated by major needs for accurate bad data detection and topology identification in the emerging electric energy systems. Due to the non-convex problem formulation, past methods usually reach a local optimum. This deficiency may lead to wrong bus/branch modelling and inappropriate noise assumption, causing significantly biased state estimate, incorrect system operation, and user cutoff. To overcome the local optimum issue, the authors propose in this study how to convexify bad data detection and topology identification problems to efficiently locate a global optimum result. To reduce relaxation error in the convexification procedure, a nuclear norm penalty is added to better approximate the original problems. Finally, they propose a new metric to evaluate the detection and identification results, which enables system operator to know how confidence one is for further system operations. Simulation results performed for several IEEE test systems show promising results for the future smart grid in improved accuracy.

Original languageEnglish (US)
Pages (from-to)2760-2767
Number of pages8
JournalIET Generation, Transmission and Distribution
Volume9
Issue number16
DOIs
StatePublished - Dec 3 2015
Externally publishedYes

Fingerprint

Error detection
Electric power systems
Topology

ASJC Scopus subject areas

  • Control and Systems Engineering
  • Energy Engineering and Power Technology
  • Electrical and Electronic Engineering

Cite this

Convexification of bad data and topology error detection and identification problems in AC electric power systems. / Weng, Yang; Ilić, Marija D.; Li, Qiao; Negi, Rohit.

In: IET Generation, Transmission and Distribution, Vol. 9, No. 16, 03.12.2015, p. 2760-2767.

Research output: Contribution to journalArticle

@article{d7c12f745d6847d4a22e2941943e23e0,
title = "Convexification of bad data and topology error detection and identification problems in AC electric power systems",
abstract = "This study is motivated by major needs for accurate bad data detection and topology identification in the emerging electric energy systems. Due to the non-convex problem formulation, past methods usually reach a local optimum. This deficiency may lead to wrong bus/branch modelling and inappropriate noise assumption, causing significantly biased state estimate, incorrect system operation, and user cutoff. To overcome the local optimum issue, the authors propose in this study how to convexify bad data detection and topology identification problems to efficiently locate a global optimum result. To reduce relaxation error in the convexification procedure, a nuclear norm penalty is added to better approximate the original problems. Finally, they propose a new metric to evaluate the detection and identification results, which enables system operator to know how confidence one is for further system operations. Simulation results performed for several IEEE test systems show promising results for the future smart grid in improved accuracy.",
author = "Yang Weng and Ilić, {Marija D.} and Qiao Li and Rohit Negi",
year = "2015",
month = "12",
day = "3",
doi = "10.1049/iet-gtd.2015.0191",
language = "English (US)",
volume = "9",
pages = "2760--2767",
journal = "IET Generation, Transmission and Distribution",
issn = "1751-8687",
publisher = "Institution of Engineering and Technology",
number = "16",

}

TY - JOUR

T1 - Convexification of bad data and topology error detection and identification problems in AC electric power systems

AU - Weng, Yang

AU - Ilić, Marija D.

AU - Li, Qiao

AU - Negi, Rohit

PY - 2015/12/3

Y1 - 2015/12/3

N2 - This study is motivated by major needs for accurate bad data detection and topology identification in the emerging electric energy systems. Due to the non-convex problem formulation, past methods usually reach a local optimum. This deficiency may lead to wrong bus/branch modelling and inappropriate noise assumption, causing significantly biased state estimate, incorrect system operation, and user cutoff. To overcome the local optimum issue, the authors propose in this study how to convexify bad data detection and topology identification problems to efficiently locate a global optimum result. To reduce relaxation error in the convexification procedure, a nuclear norm penalty is added to better approximate the original problems. Finally, they propose a new metric to evaluate the detection and identification results, which enables system operator to know how confidence one is for further system operations. Simulation results performed for several IEEE test systems show promising results for the future smart grid in improved accuracy.

AB - This study is motivated by major needs for accurate bad data detection and topology identification in the emerging electric energy systems. Due to the non-convex problem formulation, past methods usually reach a local optimum. This deficiency may lead to wrong bus/branch modelling and inappropriate noise assumption, causing significantly biased state estimate, incorrect system operation, and user cutoff. To overcome the local optimum issue, the authors propose in this study how to convexify bad data detection and topology identification problems to efficiently locate a global optimum result. To reduce relaxation error in the convexification procedure, a nuclear norm penalty is added to better approximate the original problems. Finally, they propose a new metric to evaluate the detection and identification results, which enables system operator to know how confidence one is for further system operations. Simulation results performed for several IEEE test systems show promising results for the future smart grid in improved accuracy.

UR - http://www.scopus.com/inward/record.url?scp=84949196620&partnerID=8YFLogxK

UR - http://www.scopus.com/inward/citedby.url?scp=84949196620&partnerID=8YFLogxK

U2 - 10.1049/iet-gtd.2015.0191

DO - 10.1049/iet-gtd.2015.0191

M3 - Article

AN - SCOPUS:84949196620

VL - 9

SP - 2760

EP - 2767

JO - IET Generation, Transmission and Distribution

JF - IET Generation, Transmission and Distribution

SN - 1751-8687

IS - 16

ER -