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Artificial Intelligence-Based Secured Power Grid Protocol for Smart City

Due to the modern power system’s rapid development, more scattered smart grid components are securely linked into the power system by encircling a wide electrical power network with the underpinning communication system. By enabling a wide range of applications, such as distributed energy management...

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Autores principales: Sulaiman, Adel, Nagu, Bharathiraja, Kaur, Gaganpreet, Karuppaiah, Pradeepa, Alshahrani, Hani, Reshan, Mana Saleh Al, AlYami, Sultan, Shaikh, Asadullah
Formato: Online Artículo Texto
Lenguaje:English
Publicado: MDPI 2023
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10574949/
https://www.ncbi.nlm.nih.gov/pubmed/37836846
http://dx.doi.org/10.3390/s23198016
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author Sulaiman, Adel
Nagu, Bharathiraja
Kaur, Gaganpreet
Karuppaiah, Pradeepa
Alshahrani, Hani
Reshan, Mana Saleh Al
AlYami, Sultan
Shaikh, Asadullah
author_facet Sulaiman, Adel
Nagu, Bharathiraja
Kaur, Gaganpreet
Karuppaiah, Pradeepa
Alshahrani, Hani
Reshan, Mana Saleh Al
AlYami, Sultan
Shaikh, Asadullah
author_sort Sulaiman, Adel
collection PubMed
description Due to the modern power system’s rapid development, more scattered smart grid components are securely linked into the power system by encircling a wide electrical power network with the underpinning communication system. By enabling a wide range of applications, such as distributed energy management, system state forecasting, and cyberattack security, these components generate vast amounts of data that automate and improve the efficiency of the smart grid. Due to traditional computer technologies’ inability to handle the massive amount of data that smart grid systems generate, AI-based alternatives have received a lot of interest. Long Short-Term Memory (LSTM) and recurrent Neural Networks (RNN) will be specifically developed in this study to address this issue by incorporating the adaptively time-developing energy system’s attributes to enhance the model of the dynamic properties of contemporary Smart Grid (SG) that are impacted by Revised Encoding Scheme (RES) or system reconfiguration to differentiate LSTM changes & real-time threats. More specifically, we provide a federated instructional strategy for consumer sharing of power data to Power Grid (PG) that is supported by edge clouds, protects consumer privacy, and is communication-efficient. They then design two optimization problems for Energy Data Owners (EDO) and energy service operations, as well as a local information assessment method in Federated Learning (FL) by taking non-independent and identically distributed (IID) effects into consideration. The test results revealed that LSTM had a longer training duration, four hidden levels, and higher training loss than other models. The provided method works incredibly well in several situations to identify FDIA. The suggested approach may successfully induce EDOs to employ high-quality local models, increase the payout of the ESP, and decrease task latencies, according to extensive simulations, which are the last points. According to the verification results, every assault sample could be effectively recognized utilizing the current detection methods and the LSTM RNN-based structure created by Smart.
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spelling pubmed-105749492023-10-14 Artificial Intelligence-Based Secured Power Grid Protocol for Smart City Sulaiman, Adel Nagu, Bharathiraja Kaur, Gaganpreet Karuppaiah, Pradeepa Alshahrani, Hani Reshan, Mana Saleh Al AlYami, Sultan Shaikh, Asadullah Sensors (Basel) Article Due to the modern power system’s rapid development, more scattered smart grid components are securely linked into the power system by encircling a wide electrical power network with the underpinning communication system. By enabling a wide range of applications, such as distributed energy management, system state forecasting, and cyberattack security, these components generate vast amounts of data that automate and improve the efficiency of the smart grid. Due to traditional computer technologies’ inability to handle the massive amount of data that smart grid systems generate, AI-based alternatives have received a lot of interest. Long Short-Term Memory (LSTM) and recurrent Neural Networks (RNN) will be specifically developed in this study to address this issue by incorporating the adaptively time-developing energy system’s attributes to enhance the model of the dynamic properties of contemporary Smart Grid (SG) that are impacted by Revised Encoding Scheme (RES) or system reconfiguration to differentiate LSTM changes & real-time threats. More specifically, we provide a federated instructional strategy for consumer sharing of power data to Power Grid (PG) that is supported by edge clouds, protects consumer privacy, and is communication-efficient. They then design two optimization problems for Energy Data Owners (EDO) and energy service operations, as well as a local information assessment method in Federated Learning (FL) by taking non-independent and identically distributed (IID) effects into consideration. The test results revealed that LSTM had a longer training duration, four hidden levels, and higher training loss than other models. The provided method works incredibly well in several situations to identify FDIA. The suggested approach may successfully induce EDOs to employ high-quality local models, increase the payout of the ESP, and decrease task latencies, according to extensive simulations, which are the last points. According to the verification results, every assault sample could be effectively recognized utilizing the current detection methods and the LSTM RNN-based structure created by Smart. MDPI 2023-09-22 /pmc/articles/PMC10574949/ /pubmed/37836846 http://dx.doi.org/10.3390/s23198016 Text en © 2023 by the authors. https://creativecommons.org/licenses/by/4.0/Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/).
spellingShingle Article
Sulaiman, Adel
Nagu, Bharathiraja
Kaur, Gaganpreet
Karuppaiah, Pradeepa
Alshahrani, Hani
Reshan, Mana Saleh Al
AlYami, Sultan
Shaikh, Asadullah
Artificial Intelligence-Based Secured Power Grid Protocol for Smart City
title Artificial Intelligence-Based Secured Power Grid Protocol for Smart City
title_full Artificial Intelligence-Based Secured Power Grid Protocol for Smart City
title_fullStr Artificial Intelligence-Based Secured Power Grid Protocol for Smart City
title_full_unstemmed Artificial Intelligence-Based Secured Power Grid Protocol for Smart City
title_short Artificial Intelligence-Based Secured Power Grid Protocol for Smart City
title_sort artificial intelligence-based secured power grid protocol for smart city
topic Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10574949/
https://www.ncbi.nlm.nih.gov/pubmed/37836846
http://dx.doi.org/10.3390/s23198016
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