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

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.

Automated summary

This study proposes a reliable large-scale energy management framework for renewable hybrid AC-DC microgrids (MGs) in islanded and grid-connected operations. The framework considers various renewable energy resources, distributed power generation units, energy storage, and plug-in hybrid electric vehicles (PHEV). It uses a bat optimization algorithm (BOA) to minimize operating costs and introduces an intrusion detection system (IDS) based on sequential hypothesis testing (SHT) to detect identity-enabled cyber-attacks on wireless-enabled advanced metering infrastructures (AMI).