Cross-Layer Attack Path Exploration for Smart Grid Based on Knowledge of Target Network

2018 ◽  
pp. 433-441
Author(s):  
WenJie Kang ◽  
PeiDong Zhu ◽  
Gang Hu ◽  
Zhi Hang ◽  
Xin Liu
Keyword(s):  
Smart Grid ◽  
Cross Layer ◽  
Target Network ◽  
Attack Path ◽  
Grid Based

Load forecasting for smart grid based on continuous-learning neural network

2021 ◽  
Vol 201 ◽  
pp. 107545
Author(s):  
Marcela A. da Silva ◽  
Thays Abreu ◽  
Carlos Roberto Santos-Júnior ◽  
Carlos R. Minussi
Keyword(s):  
Neural Network ◽  
Smart Grid ◽  
Load Forecasting ◽  
Continuous Learning ◽  
Grid Based

scholarly journals Cross Layer Optimization and Simulation of Smart Grid Home Area Network

2018 ◽  
Vol 2018 ◽  
pp. 1-14
Author(s):  
Lipi K. Chhaya ◽  
Paawan Sharma ◽  
Adesh Kumar ◽  
Govind Bhagwatikar
Keyword(s):  
Smart Grid ◽  
Network Performance ◽  
Performance Enhancement ◽  
Hierarchical Architecture ◽  
Buffer Size ◽  
Cross Layer ◽  
Area Network ◽  
Frame Aggregation ◽  
Cross Layer Optimization ◽  
Layered Approach

An electrical “Grid” is a network that carries electricity from power plants to customer premises. Smart Grid is an assimilation of electrical and communication infrastructure. Smart Grid is characterized by bidirectional flow of electricity and information. Smart Grid is a complex network with hierarchical architecture. Realization of complete Smart Grid architecture necessitates diverse set of communication standards and protocols. Communication network protocols are engineered and established on the basis of layered approach. Each layer is designed to produce an explicit functionality in association with other layers. Layered approach can be modified with cross layer approach for performance enhancement. Complex and heterogeneous architecture of Smart Grid demands a deviation from primitive approach and reworking of an innovative approach. This paper describes a joint or cross layer optimization of Smart Grid home/building area network based on IEEE 802.11 standard using RIVERBED OPNET network design and simulation tool. The network performance can be improved by selecting various parameters pertaining to different layers. Simulation results are obtained for various parameters such as WLAN throughput, delay, media access delay, and retransmission attempts. The graphical results show that various parameters have divergent effects on network performance. For example, frame aggregation decreases overall delay but the network throughput is also reduced. To prevail over this effect, frame aggregation is used in combination with RTS and fragmentation mechanisms. The results show that this combination notably improves network performance. Higher value of buffer size considerably increases throughput but the delay is also greater and thus the choice of optimum value of buffer size is inevitable for network performance optimization. Parameter optimization significantly enhances the performance of a designed network. This paper is expected to serve as a comprehensive analysis and performance enhancement of communication standard suitable for Smart Grid HAN applications.

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