Real-Time Machine Learning-based fault Detection, Classification, and locating in large scale solar Energy-Based Systems: Digital twin simulation

被引：18

作者：

Cao, Hanhua ^{[1
]}

Zhang, Dongming ^{[2
]}

Yi, Shujuan ^{[2
]}

机构：

[1] Guangzhou Xinhua Univ, Sch Informat & Intelligent Engn, Guangzhou 510520, Peoples R China

[2] Heilongjiang Bayi Agr Univ, Coll Engn, Daqing 163000, Peoples R China

来源：

SOLAR ENERGY | 2023年 / 251卷

关键词：

Energy management; Machine learning; Bat optimization algorithm; Microgrid; Cyber security; NETWORKED MICROGRIDS; THINGS;

D O I：

10.1016/j.solener.2022.12.042

中图分类号：

TE [石油、天然气工业]; TK [能源与动力工程];

学科分类号：

0807 ; 0820 ;

摘要：

The current study considers numerous renewable energy resources, distributed power generation units, energy storage, and plug-in hybrid electric vehicles (PHEV) in order to propose a reliable large-scale energy management framework that can be applied to islanded and grid-connected operations of renewable hybrid AC-DC microgrids (MGs). The framework uses a bat optimization algorithm (BOA) for minimizing the operating costs of the network and in addition introduces an intrusion detection system (IDS) according to the sequential hypothesis testing (SHT) method for detecting identity-enabled cyber-attacks (i.e classification of Sybil attacks, masquerading attacks) on the wireless-enabled advanced metering infrastructures (AMI). The suggested IDS uses the received signal strength (RSS) amount for distinguishing various signal resources and detecting cyberattacks. An IEEE 33-bus testing system has been used to construct a real-time hybrid MG in order to determine the reliability and efficiency of the suggested framework.

引用

页码：77 / 85

页数：9

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