Benchmark of machine learning algorithms on capturing future distribution network anomalies. Issue 8 (3rd April 2019)

• Record Type: Journal Article
• Authors: Mohammadpourfard, Mostafa; Weng, Yang; Tajdinian, Mohsen
• Is Part Of: IET generation, transmission & distribution. Volume 13:Issue 8(2019)
• Page Start: 1441
• Page End: 1455
• Publication Date: 2019-04-03
• Subjects: learning (artificial intelligence) -- security of data -- distributed power generation -- smart power grids -- electric vehicles -- distribution networks; attack detection problem -- future distribution network anomalies -- conventional distribution network -- active grid -- smart grid technologies -- smart distribution grid -- attacker -- attacks -- distribution management system -- distribution system -- IEEE distribution test systems -- modern distribution grid -- transmission -- distribution systems; Electric power production -- Periodicals; Electric power transmission -- Periodicals; Electric power distribution -- Periodicals; 621.3105
• DOI: 10.1049/iet-gtd.2018.6801
• Languages: English
• ISSNs: 1751-8687

Abstract

Abstract : The conventional distribution network is undergoing structural changes and becoming an active grid due to the advent of smart grid technologies encompassing distributed energy resources (DERs), aggregated demand response and electric vehicles (EVs). This establishes a need for state estimation‐based tools and real‐time monitoring of the distribution grid to correctly apply active controls. Although such new tools may be vulnerable to cyber‐attacks, cyber‐security of distribution grid has not received enough attention. As smart distribution grid intensively relies on communication infrastructures, the authors assume in this study that an attacker can compromise the communication and successfully conduct attacks against crucial functions of the distribution management system, making the distribution system prone to instability boundaries for collapses. They formulate the attack detection problem in the distribution grid as a statistical learning problem and demonstrate a comprehensive benchmark of statistical learning methods on various IEEE distribution test systems. The proposed learning algorithms are tested using various attack scenarios which include distinct features of modern distribution grid such as integration of DERs and EVs. Furthermore, the interaction between transmission and distribution systems and its effect on the attack detection problem are investigated. Simulation results show attack detection is more challenging in the distribution grid.