Machine Learning and Internet-of-Things Solutions for Microgrid Resilient Operation

Microgrids inherently have lower inertia in comparison with the power system due to the high penetration of renewable generation sources. On the other hand, the lower spring reserve, high dynamic loads, and inherent volatility of renewable generation sources have demanded real-time control of them. The development of intelligent electrical devices, communication infrastructure, and Internet-of-Things (IoT) technology has enhanced real-time microgrid operation. In contrast, they have exposed the smart microgrid to cyber malicious activities. Cyber attackers try to disrupt the microgrid operation by manipulating the sensors, control center, communication infrastructure, and intelligent electrical devices, leading to decreasing power quality, cascading instability, and, finally, blackout. The effect of a cyber-attack in a standalone microgrid will be more distractive. Given the advantages inherent in these technologies, this chapter conducts an extensive examination of their integration into the management, control, and upkeep of both onshore and offshore microgrids. It provides a detailed account of the communication infrastructure within microgrids and identifies potential vulnerabilities. The chapter then elucidates methods for detecting cyber-attacks, placing a particular emphasis on machine learning, IoT, and Digital Twins. Subsequently, the chapter delves into the convergence of these technologies with maritime microgrids. It expounds upon how these advancements render maritime microgrids susceptible to cyber threats and emphasizes the instrumental role of machine learning in fortifying their cyber resilience. Furthermore, the chapter explores the application of IoT and Digital Twins, combined with machine learning, to enhance the maintenance and operational resilience of maritime microgrids. Finally, the chapter addresses the technical challenges faced by smart seaports and offers resilient operational solutions to mitigate potential disruptions.