A novel fault diagnosis method of smart grids based on memory spiking neural P systems considering measurement tampering attacks

Cyber-attacks can tamper with measurement data from physical systems via communication networks of smart grids, which could potentially lead circuit breakers to trip creating a false fault in the absence of any faulty section. Accordingly, a fault diagnosis method should first determine whether a fa...

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Main Authors: Wang, Tao, Liu, Wei, Cabrera, Luis Valencia, Wang, Peng, Wei, Xiaoguang, Zang, Tianlei
Other Authors: School of Electrical and Electronic Engineering
Format: Article
Language:English
Published: 2022
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Online Access:https://hdl.handle.net/10356/163876
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Institution: Nanyang Technological University
Language: English
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spelling sg-ntu-dr.10356-1638762022-12-21T02:09:34Z A novel fault diagnosis method of smart grids based on memory spiking neural P systems considering measurement tampering attacks Wang, Tao Liu, Wei Cabrera, Luis Valencia Wang, Peng Wei, Xiaoguang Zang, Tianlei School of Electrical and Electronic Engineering Engineering::Electrical and electronic engineering Fault Diagnosis Membrane Computing Cyber-attacks can tamper with measurement data from physical systems via communication networks of smart grids, which could potentially lead circuit breakers to trip creating a false fault in the absence of any faulty section. Accordingly, a fault diagnosis method should first determine whether a fault is actually present; however, current diagnosis methods of power systems struggle to achieve this goal. This paper proposes a novel method for fault diagnosis based on memory spiking neural P systems, which can distinguish false faults caused by measurement tampering attacks. The proposed method consists of three modules with the functions of suspicious fault section detection, measurement tamper attack identification and fault diagnosis, respectively. The suspicious fault section detection module is used to find candidate sections to reduce the fault diagnosis scope. The attack identification module is designed to identify whether a possibly faulty section is under the measurement tampering attack or not. The fault diagnosis module is devised to diagnose true faults, detecting both the fault sections and their corresponding fault types. To achieve the above goals, inspired by the memory recall mechanism of human brains, a memory spiking neural P system and a corresponding general matrix reasoning algorithm are proposed, which can synthetically utilize the remote measurements and remote signals via a new modeling mechanism. Finally, case studies based on the IEEE 14 and IEEE 118 bus systems verify the feasibility and effectiveness of the proposed method. This research was partially funded by grants from the National Natural Science Foundation of China (61703345, 51907097), the Chunhui Project Foundation of the Education Department of China (Z201980), the Open Research Subject of Key Laboratory of Fluid and Power Machinery (Xihua University), Ministry of Education (szjj2019-27) and the Young Scholars Reserve Talents Support Project of Xihua University. The participation of Luis Valencia was also supported in Spain by FEDER/Ministerio de Ciencia e Innovación C Agencia Estatal de Investigación/Project TIN2017-89842-P. 2022-12-21T02:09:34Z 2022-12-21T02:09:34Z 2022 Journal Article Wang, T., Liu, W., Cabrera, L. V., Wang, P., Wei, X. & Zang, T. (2022). A novel fault diagnosis method of smart grids based on memory spiking neural P systems considering measurement tampering attacks. Information Sciences, 596, 520-536. https://dx.doi.org/10.1016/j.ins.2022.03.013 0020-0255 https://hdl.handle.net/10356/163876 10.1016/j.ins.2022.03.013 2-s2.0-85126525206 596 520 536 en Information Sciences © 2022 Elsevier Inc. All rights reserved.
institution Nanyang Technological University
building NTU Library
continent Asia
country Singapore
Singapore
content_provider NTU Library
collection DR-NTU
language English
topic Engineering::Electrical and electronic engineering
Fault Diagnosis
Membrane Computing
spellingShingle Engineering::Electrical and electronic engineering
Fault Diagnosis
Membrane Computing
Wang, Tao
Liu, Wei
Cabrera, Luis Valencia
Wang, Peng
Wei, Xiaoguang
Zang, Tianlei
A novel fault diagnosis method of smart grids based on memory spiking neural P systems considering measurement tampering attacks
description Cyber-attacks can tamper with measurement data from physical systems via communication networks of smart grids, which could potentially lead circuit breakers to trip creating a false fault in the absence of any faulty section. Accordingly, a fault diagnosis method should first determine whether a fault is actually present; however, current diagnosis methods of power systems struggle to achieve this goal. This paper proposes a novel method for fault diagnosis based on memory spiking neural P systems, which can distinguish false faults caused by measurement tampering attacks. The proposed method consists of three modules with the functions of suspicious fault section detection, measurement tamper attack identification and fault diagnosis, respectively. The suspicious fault section detection module is used to find candidate sections to reduce the fault diagnosis scope. The attack identification module is designed to identify whether a possibly faulty section is under the measurement tampering attack or not. The fault diagnosis module is devised to diagnose true faults, detecting both the fault sections and their corresponding fault types. To achieve the above goals, inspired by the memory recall mechanism of human brains, a memory spiking neural P system and a corresponding general matrix reasoning algorithm are proposed, which can synthetically utilize the remote measurements and remote signals via a new modeling mechanism. Finally, case studies based on the IEEE 14 and IEEE 118 bus systems verify the feasibility and effectiveness of the proposed method.
author2 School of Electrical and Electronic Engineering
author_facet School of Electrical and Electronic Engineering
Wang, Tao
Liu, Wei
Cabrera, Luis Valencia
Wang, Peng
Wei, Xiaoguang
Zang, Tianlei
format Article
author Wang, Tao
Liu, Wei
Cabrera, Luis Valencia
Wang, Peng
Wei, Xiaoguang
Zang, Tianlei
author_sort Wang, Tao
title A novel fault diagnosis method of smart grids based on memory spiking neural P systems considering measurement tampering attacks
title_short A novel fault diagnosis method of smart grids based on memory spiking neural P systems considering measurement tampering attacks
title_full A novel fault diagnosis method of smart grids based on memory spiking neural P systems considering measurement tampering attacks
title_fullStr A novel fault diagnosis method of smart grids based on memory spiking neural P systems considering measurement tampering attacks
title_full_unstemmed A novel fault diagnosis method of smart grids based on memory spiking neural P systems considering measurement tampering attacks
title_sort novel fault diagnosis method of smart grids based on memory spiking neural p systems considering measurement tampering attacks
publishDate 2022
url https://hdl.handle.net/10356/163876
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