Link importance-based network recovery for large-scale failures in smart grids

2020 ◽  
Author(s):  
Huibin Jia ◽  
Yonghe Gai ◽  
Dongfang Xu ◽  
Yincheng Qi ◽  
Hongda Zheng
Keyword(s):  
Smart Grids ◽  
Large Scale ◽  
Network Recovery

scholarly journals Large-scale smart grids as system of systems

2013 ◽  
Author(s):  
Jennifer Pérez ◽  
Jessica Díaz ◽  
Juan Garbajosa ◽  
Agustín Yagüe ◽  
Eloy Gonzalez ◽  
...  
Keyword(s):  
Smart Grids ◽  
Large Scale ◽  
System Of Systems

scholarly journals An integral control formulation of mean field game based large scale coordination of loads in smart grids

Automatica ◽  
2019 ◽  
Vol 100 ◽  
pp. 312-322 ◽  
Author(s):  
Arman C. Kizilkale ◽  
Rabih Salhab ◽  
Roland P. Malhamé
Keyword(s):  
Smart Grids ◽  
Large Scale ◽  
Mean Field ◽  
Integral Control ◽  
Mean Field Game

scholarly journals Short Time Solar Power Forecasting Using Persistence Extreme Learning Machine Approach

2021 ◽  
Vol 294 ◽  
pp. 01002
Author(s):  
Xiaoyan Xiang ◽  
Yao Sun ◽  
Xiaofei Deng
Keyword(s):  
Solar Energy ◽  
Extreme Learning Machine ◽  
Power Output ◽  
Smart Grids ◽  
Large Scale ◽  
Solar Power ◽  
Learning Algorithm ◽  
Power Performance ◽  
Energy Prediction ◽  
Learning Machine

Solar energy in nature is irregular, so photovoltaic (PV) power performance is intermittent, and highly dependent on solar radiation, temperature and other meteorological parameters. Accurately predicting solar power to ensure the economic operation of micro-grids (MG) and smart grids is an important challenge to improve the large-scale application of PV to traditional power systems. In this paper, a hybrid machine learning algorithm is proposed to predict solar power accurately, and Persistence Extreme Learning Machine(P-ELM) algorithm is used to train the system. The input parameters are the temperature, sunshine and solar power output at the time of i, and the output parameters are the temperature, sunshine and solar power output at the time i+1. The proposed method can realize the prediction of solar power output 20 minutes in advance. Mean absolute error (MAE) and root-mean-square error (RMSE) are used to characterize the performance of P-ELM algorithm, and compared with ELM algorithm. The results show that the accuracy of P-ELM algorithm is better in short-term prediction, and P-ELM algorithm is very suitable for real-time solar energy prediction accuracy and reliability.

scholarly journals A Deep and Scalable Unsupervised Machine Learning System for Cyber-Attack Detection in Large-Scale Smart Grids

IEEE Access ◽  
2019 ◽  
Vol 7 ◽  
pp. 80778-80788 ◽  
Author(s):  
Hadis Karimipour ◽  
Ali Dehghantanha ◽  
Reza M. Parizi ◽  
Kim-Kwang Raymond Choo ◽  
Henry Leung
Keyword(s):  
Machine Learning ◽  
Smart Grids ◽  
Large Scale ◽  
Attack Detection ◽  
Learning System ◽  
Cyber Attack ◽  
Unsupervised Machine Learning

scholarly journals Future Generation 5G Wireless Networks for Smart Grid: A Comprehensive Review

Energies ◽  
2019 ◽  
Vol 12 (11) ◽  
pp. 2140 ◽  
Author(s):  
Sofana Reka. S ◽  
Tomislav Dragičević ◽  
Pierluigi Siano ◽  
S.R. Sahaya Prabaharan
Keyword(s):  
Smart Grid ◽  
Electricity Markets ◽  
Smart Grids ◽  
Large Scale ◽  
Business Models ◽  
Future Generation ◽  
Current Status ◽  
Complete Analysis ◽  
5G Networks ◽  
5G Network

Wireless cellular networks are emerging to take a strong stand in attempts to achieve pervasive large scale obtainment, communication, and processing with the evolution of the fifth generation (5G) network. Both the present day cellular technologies and the evolving new age 5G are considered to be advantageous for the smart grid. The 5G networks exhibit relevant services for critical and timely applications for greater aspects in the smart grid. In the present day electricity markets, 5G provides new business models to the energy providers and improves the way the utility communicates with the grid systems. In this work, a complete analysis and a review of the 5G network and its vision regarding the smart grid is exhibited. The work discusses the present day wireless technologies, and the architectural changes for the past years are shown. Furthermore, to understand the user-based analyses in a smart grid, a detailed analysis of 5G architecture with the grid perspectives is exhibited. The current status of 5G networks in a smart grid with a different analysis for energy efficiency is vividly explained in this work. Furthermore, focus is emphasized on future reliable smart grid communication with future roadmaps and challenges to be faced. The complete work gives an in-depth understanding of 5G networks as they pertain to future smart grids as a comprehensive analysis.

scholarly journals Simulation-Based Evaluation of the Performance of Broadband over Power Lines with Multiple Repeaters in Linear Topology of Distribution Substations

2020 ◽  
Vol 10 (19) ◽  
pp. 6879
Author(s):  
Petr Musil ◽  
Petr Mlynek ◽  
Jan Slacik ◽  
Jiri Pokorny
Keyword(s):  
Smart Grids ◽  
Large Scale ◽  
Field Measurements ◽  
Lessons Learned ◽  
Power Lines ◽  
Multiple Streams ◽  
Linear Topology ◽  
Performance Improvements ◽  
Multiple Data ◽  
The Impact

Broadband over Power Lines (BPL) is considered a promising communication technology in the concept of Smart Grids. This paper evaluates networks based on BPL, with a focus on the impact of repeaters in the linear topology of distribution substations. In large-scale Smart Grids network planning, positions of repeaters have to be carefully chosen. This article should help to determine such positions and limitations of BPL linear topology networks. Laboratory and on-field measurements and their results are presented in this article. Results show the impact of repeater’s deployment for different testing methodologies also with regard to other already presented studies. Measured values and the determined impacts of repeaters are later used as input data for simulation of the linear BPL topology in terms of network throughput with multiple streams and bottlenecks. These occur especially on lines shared by multiple communicating nodes. Furthermore, the simulation investigates the balancing time of multiple data streams throughput. The simulation shows that the throughput balancing can occupy a significant time slot, up to tens of seconds before the throughput of different streams balances. Also, the more data is generated, the more time the balancing time takes. Additionally, the throughput drop caused by a repeater is determined into the range of 35–60%. Based on the measurement and simulation results, lessons learned are presented, and possible performance improvements are discussed.

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