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

Content Provider	IET Digital Library
Author	Weng, Yang Ilić, Marija D. Li, Qiao Negi, Rohit
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.
Starting Page	2760
Ending Page	2767
Page Count	8
ISSN	17518687
Volume Number	9
e-ISSN	17518695
Issue Number	Issue 16, Dec (2015)
Alternate Webpage(s)	https://digital-library.theiet.org/content/journals/iet-gtd/9/16
Alternate Webpage(s)	https://digital-library.theiet.org/content/journals/10.1049/iet-gtd.2015.0191
Journal	IET Generation, Transmission & Distribution
Publisher Date	2015-12-03
Access Restriction	Open
Rights Holder	© The Institution of Engineering and Technology
Subject Keyword	AC Electric Power System Bad Data Convexification Bad Data Detection Convexification Procedure IEEE Test System Noise Assumption Nonconvex Problem Formulation Power System Relaxation Error Reduction Smart Grid Smart Power Grid Topology Topology Error Detection Topology Identification Problem
Content Type	Text
Resource Type	Article
Subject	Control and Systems Engineering Energy Engineering and Power Technology Electrical and Electronic Engineering

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