Optimally detecting and classifying the transmission line fault in power system using hybrid technique

Rajesh, P.; Kannan, R.; Vishnupriyan, J.; Rajani, B.

doi:10.1016/j.isatra.2022.03.017

Cited by 52 publications

(18 citation statements)

References 32 publications

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“…However, current digital twin solutions for differential protection applications in distribution networks are yet to be explored. Differential protection devices on both sides of transmission lines in distribution networks disconnect the lines in the event of electrical failure [8]. Actions such as the collection of electrical quantity information from differential protection devices and disconnection of faults can be mapped to the digital twin system as the source of its closed-loop information interaction.…”

Section: Introductionmentioning

confidence: 99%

Digital twin-based 5G+ longitudinal differential protection method

Wang¹,

Zhang²,

Cai³

et al. 2023

International Conference on Signal Processing and Communication Technology (SPCT 2022)

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To address the problems of difficult data synchronization and insufficient protection capability of transmission line longitudinal protection, this paper proposes a digital twin solution for transmission line longitudinal differential protection scenarios. A 5G communication system is also introduced to collect and interact with the electrical quantities at both ends of the transmission line. Through the digital twin modeling of the entire transmission line, various electrical quantities of the simulated distribution network transmission line are obtained. And the obtained modeled values are used for current prediction by BP neural network. Experiments demonstrate that the method provides multiple insurances for longitudinal differential protection by analyzing the data generated by the digital twin for prediction.

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Section: Introductionmentioning

confidence: 99%

Digital twin-based 5G+ longitudinal differential protection method

Wang¹,

Zhang²,

Cai³

et al. 2023

International Conference on Signal Processing and Communication Technology (SPCT 2022)

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“…9,10 In general, the IoT applications help to transmit IPv6 packets in WSN nodes; these features lead to numerous profits to users. 11,12 If many IoT devices are linked and interconnected, then the IoT applications create marvelous IoT traffic. 13,14 Subsequently, IoT traffic is for communication among devices; the communication consistency is dangerous, particularly in a moderately unbalanced WSN when compared to the wired network.…”

Section: Introductionmentioning

confidence: 99%

Hybrid Marine Predators and Border Collie Optimization algorithm for multipath routing in IoT

Kumar

2023

Int J Communication

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SummaryInternet of Things (IoT) is a network of smart things that connects people and objects for gathering and exchanging information by means of embedded sensor. IoT‐based wireless sensor network (WSN) nodes are monitored as a resource parameter at the variety of methods, including resource calculation, energy resource, and storage resource, with multipath routing protocols necessary to enable extended network lifespan and achieve high energy utilization. The existing routing protocols did not provide optimal path from the multipath route. Hence, in this manuscript, a Hybrid Marine Predators and Border Collie Optimization algorithm is proposed for multipath routing in IoTs. The main aim of this endeavor is to “increase network lifetime.” IoT is simulated initially and multipath routing initiates in internet of things network. Multipath routing is achieved by proposed Hyb‐MP‐BCOA; this is the combination of Hybrid Marine Predators Algorithm and Border Collie Optimization algorithm. The proposed Hyb‐MP‐BCOA algorithm chooses the optimum path from the multiple path obtainable for routing, depending upon fitness parameter, like context awareness, network lifetime, residual energy, trust, and delay. Based on the fitness parameter, the optimal path is selected by using Hyb‐MP‐BCOA algorithm. Finally, the proposed Hyb‐MP‐BCOA‐MR‐IoT model is applied in MATLAB, and efficacy of proposed technique is estimated by using various performance metrics, like drop, normalized network energy, lifetime of network, delay, throughput, energy consumed, packet delivery ratio, and number of sensor nodes versus aggregation level. The proposed method attains lower delay of 14.285%, 50%, 53.846%, and 57.142%, lower packet drop of 14.285%, 50%, 53.846%, and 57.142%, lower energy consumption compared with existing methods such as multiobjective sun flower optimization with gray wolf optimizer (GWO) for multipath routing in IoT (SFO‐GWO‐MR‐IoT), multiobjective Whale Lion Fireworks optimization algorithm and fitness function for multipath routing in IoT (WLFA‐MR‐IoT), multipath routing in IoT using shuffled frog leaping algorithm (SFLA‐MR‐IoT), and multipath routing in IoT using hybrid Whale Optimization Algorithm‐Moth Flame Optimization (MFO)(hyb‐WOA‐MFO‐MR‐IoT). Finally, the safe communication is attained in the IoT network.

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“…Consecutively, the cloud utility is debugged on the internet throughout the privacy is the important problem of cloud services 6,7 . Subsequently, the security of data, prevention, and intrusion detection is the main security to be impacted in the cloud infrastructure 8‐12 . Nevertheless, cloud network utilizing the incorporated technologies and virtualization models activate with the help of standard internet protocol 13 .…”

Section: Introductionmentioning

confidence: 99%

“…6,7 Subsequently, the security of data, prevention, and intrusion detection is the main security to be impacted in the cloud infrastructure. [8][9][10][11][12] Nevertheless, cloud network utilizing the incorporated technologies and virtualization models activate with the help of standard internet protocol. 13 The CC stimulates conventional attacks, such as denial of service (DoS), distributed denial of service (DDoS), and Internet protocol (IP) spoofing.…”

Section: Introductionmentioning

confidence: 99%

“…12.75%, 32.15%, 14.36%, 26.13%, and 15.93% higher sensitivity, 16.86%, 15.26%, 11.36%, 19.26%, 21.45%, and 16.25% higher specificity, 14.15%, 15.01%, 16.36%, 27.26%, 14.28%, and 15.86% higher precision, 15.73%, 11.43%, 15.26%, 29.26%, 14.27%, and 13.97% higher F-score, 38.88%, 43.42%, 21.72%, 15.23%, 32.21%, and 46.13% lower false positive rate, 5.612%, 3.025%, 47.12%, 16.24%, 24.12%, and 6.83% better AUC compared with the existing methods, like IDS-CC-DBN-CSSA, IDS-CC-DNN-IGASAA, IDS-CC-MLPNN-ABC, IDS-CC-RCNN-ALO, IDS-CC-BWO-CONV-LSTM, and IDS-CC-OCNN-HMLSTM, respectively. CC environment gains more attention among the individual user and the organization.…”

mentioning

confidence: 99%

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Intrusion detection framework for securing privacy attack in cloud computing environment using DCCGAN‐RFOA

Devi

Muthusenthil

2022

Trans Emerging Tel Tech

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Cloud computing (CC) is vulnerable for the attacks of current information technology, because it prolongs and uses the information technology infrastructure, traditional operating system, and applications. Several security issues in detecting irregular network performances are confronted by the CC environment. With the intention of solving these security issues, dual-channel capsule generation adversarial network optimized with red fox optimization algorithm fostered intrusion detection framework (IDS-CC-DCCGAN-RFOA) is proposed in this article, for securing the privacy attacks in CC environment. Initially, the data are taken from NSL-KDD benchmark dataset. Then the data are given to the preprocessing segment. When using the developed random forest and local least squares in preprocessing segment, the redundancy, and missing values are purged. Then the output of preprocessing is given to the feature selection stage. In feature selection, the optimal features are extracted using univariate ensemble feature selection (UEFS) technique. Based on the optimal features, the data are classified into secured data and privacy attack data based on dual-channel capsule generative adversarial network. Here, the weight of dual-channel capsule generative adversarial network is optimally tuned using the red fox optimization algorithm for generating an effective and optimal solution to detect the intruders. The proposed IDS-CC-DCCGAN-RFOA method is implemented in MATLAB. Here, the performance metrics, like accuracy, specificity, sensitivity, F-score, precision, false positive rate, AUC are analyzed.Then, the proposed IDS-CC-CCGAN-WSOA method provides 13.9367%, 11.12%, 13.268%, and 13.739% higher accuracy and 24.54%, 29.76%, 12.87%, and 34.21% higher sensitivity compared with the existing methods, like intrusion detection framework in cloud computing using deep belief network with salp swarm algorithm (IDS-CC-DBN-CSSA), intrusion detection framework in cloud computing using deep neural network with improved genetic algorithm and simulated annealing algorithm (IDS-CC-DNN-IGASAA), intrusion detection for cloud computing using multilayer perceptron neural network and artificial bee colony optimization algorithm (IDS-CC-MLPNN-ABC), and intrusion detection for cloud computing using recurrent convolutional neural network with ant lion optimization algorithm (IDS-CC-RCNN-ALO).

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Optimally detecting and classifying the transmission line fault in power system using hybrid technique

Cited by 52 publications

References 32 publications

Digital twin-based 5G+ longitudinal differential protection method

Digital twin-based 5G+ longitudinal differential protection method

Hybrid Marine Predators and Border Collie Optimization algorithm for multipath routing in IoT

Intrusion detection framework for securing privacy attack in cloud computing environment using DCCGAN‐RFOA

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