Deep Neural Network and Social Ski-Driver Optimization Algorithm for Power System Restoration with VSC - HVDC Technology

Ganesh, Jan Bhasha Shaik and

doi:10.46253/jcmps.v3i1.a1

Cited by 27 publications

(2 citation statements)

References 22 publications

(27 reference statements)

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“…𝑑 g is the hidden result of bit g. The Euclidean norm ||𝑋 − 𝜀 g || 2 is used to express the spacing between the input vector and the Gauss core. As can be seen from formula (1), when approaching the center of the hidden layer node, the input signal correspondingly generates a larger output.…”

Section: Structure Of Rbf Networkmentioning

confidence: 99%

“…The energy-saving evaluation of the HVDC project has specific guiding significance for its operation and maintenance. The project has a specific technical support role for improving energy conversion efficiency and reducing energy consumption [1]. It is necessary to intensely discuss the mode and method of energy-saving evaluation of converter power stations.…”

Section: Introductionmentioning

confidence: 99%

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Energy Efficiency Evaluation of HVDC Converter Station Based on Deep Neural Network Model

Rong

et al. 2023

Applied Mathematics and Nonlinear Sciences

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This paper introduces a new idea of energy efficiency evaluation of high voltage direct current (HVDC) converter based on deep neural networks. This paper analyzes the leading operating indicators and finds the main influencing factors in constructing the energy-saving hands of each converter table. Then this paper uses the deep neural network method to establish the converter table's energy efficiency model. The deep neural network for energy efficiency evaluation of the converter table is obtained. In this paper, PSCAD/EMTDC was used for simulation to evaluate the energy-saving effect. The research shows that the method of energy efficiency evaluation based on deep neural networks is feasible.

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Section: Structure Of Rbf Networkmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Energy Efficiency Evaluation of HVDC Converter Station Based on Deep Neural Network Model

Rong

et al. 2023

Applied Mathematics and Nonlinear Sciences

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An optimized radial bias function neural network for intrusion detection of distributed denial of service attack in the cloud

Varghese

Jose

2022

Concurrency and Computation

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Cloud system is a set of resources meant to provide cloud customers with on-demand services. Accessibility to cloud environment is provided through internet services, making data stored on cloud more accessible to attackers internally and externally. Several intrusion detection systems and authentication methods have been developed in previous studies to identify the intruders, however they are generally unsuccessful with certain drawbacks. Numerous existing researchers have focused on machine learning techniques for identifying intrusions. This article intends to introduce a new intrusion detection model in the cloud and it involves three processes like preprocessing, feature extraction and detection. Initially, preprocessing takes place and the preprocessed data are subjected to feature extraction process. The flow-based features, statistical, and higher-order statistical features with improved holoentropy features, and the technical indicators are extracted. After extracting the features, they are subjected to a detection process, where the optimized radial bias function neural network (RBF-NN) is exploited. For precise detection, the weight of RBF-NN is tuned optimally by (harmonic mean based poor and rich optimization) HMPRO Algorithm. The main aim of the proposed model is to detect attacks in the cloud.Finally, the performance of the presented scheme is computed over the existing approaches using the CICDDoS2019 and UNSW-NB_15 dataset in terms of different metrics.

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Optimized deep neural network based DDoS attack detection and bait mitigation process in software defined network

Perumal

Karmel

2023

Concurrency and Computation

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The software-defined network (SDN) is a new network design with an operating system that allows better network quality control. The controller's primary role in an SDN network is to divide the control and forwarding planes in order to provide essential network power. The backup controller in an SDN may confront a variety of obstacles during a DDoS attack. It disrupts the flow of the network by attacking the service nodes hence obstructing the legitimate users from getting service. To overcome these issues, this work introduces a DDoS attack detection model that will aid in removing the attacker from the network. Initially, the input data is sent into the detection phase, which identifies the existence and kind of attacks. The presence of an attack is determined based on the data flow, and statistical features. With the reference of the extracted features, the optimized deep neural network (DNN) will decide the existence of attacker in the network. To make the decision more precise, the training of DNN will be carried out by self-improved moth flame optimization (SIMFO) Algorithm via tuning the optimal weights. Once an attacker's presence has been identified, it is critical to remove the attacker's node from the network.

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Deep Neural Network and Social Ski-Driver Optimization Algorithm for Power System Restoration with VSC - HVDC Technology

Cited by 27 publications

References 22 publications

Energy Efficiency Evaluation of HVDC Converter Station Based on Deep Neural Network Model

Energy Efficiency Evaluation of HVDC Converter Station Based on Deep Neural Network Model

An optimized radial bias function neural network for intrusion detection of distributed denial of service attack in the cloud

Optimized deep neural network based DDoS attack detection and bait mitigation process in software defined network

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