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Title	Enhancing Resilient Operation Of Distributed Energy Resources Using Reliable Machine Learning-Based Iot Connectivity
ID_Doc	23914
Authors	Elsisi M.; Su C.-L.; Lin C.-H.; Ku T.-T.
Year	2024
Published	Conference Record - Industrial and Commercial Power Systems Technical Conference
DOI	http://dx.doi.org/10.1109/ICPS60943.2024.10563334
Abstract	The most realistic way to achieve large-scale integration of distributed energy resources (DERs) into the current grid system is to implement microgrids. These systems function as localized power grids that can operate independently or seamlessly integrate with utility grids. They include DERs, energy storage options, and a variety of loads. Microgrid architecture is changing toward greater distribution, intelligence, and close network integration as communication network technology develops quickly. Microgrids are useful in many fields, including as Industry 4.0, smart cities, and the Internet of Things (IoT). However, the connection of the microgrid components and transfer of data via the internet network expose the data to cyber threats, including false data injection and adversarial attacks, which deteriorates the operation data analysis models such as machine learning (ML). This paper outlines the enhancement of resilient operation using reliable IoT based on reliable ML as a case study on a practical PV farm for online monitoring of smart inverters. © 2024 IEEE.
Author Keywords	Distributed energy resources; Energy systems; IoT; Machine learning; Resilient operation

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