Rademics Research Institute

Abstract

The rapid evolution of power electronics, driven by Silicon Carbide (SiC) and Gallium Nitride (GaN) wide bandgap (WBG) semiconductors, has revolutionized the efficiency, reliability, and scalability of modern energy systems. The integration of these advanced power devices with the IoT and AI-driven smart grid infrastructure enables real-time monitoring, predictive analytics, and self-healing capabilities, significantly enhancing grid performance. The increasing complexity of IoT-integrated power systems presents critical challenges in data processing, cybersecurity, predictive maintenance, and system optimization. This book chapter explores cutting-edge advancements in SiC- and GaN-based power electronics, focusing on their role in high-efficiency grid applications. Key topics include AI-powered predictive maintenance for fault detection, digital twin technology for real-time performance optimization, and scalable big data analytics for enhancing grid intelligence. Additionally, AI-driven cybersecurity frameworks and self-healing mechanisms are examined to ensure the resilience of smart grid components against cyber threats and operational anomalies. The convergence of SiC and GaN power electronics with IoT and AI not only optimizes energy conversion efficiency but also fosters the development of autonomous, self-adaptive energy networks. Future research directions emphasize hybrid edge-cloud computing architectures, federated learning for decentralized intelligence, and advanced machine learning models for real-time power system optimization. The insights presented in this chapter provide a foundation for accelerating the adoption of SiC- and GaN-based power electronics in next-generation smart grids, driving the transition toward more sustainable, intelligent, and resilient energy infrastructure.

Introduction

The evolution of power electronics has been driven by the demand for higher efficiency, better performance, and improved reliability in energy systems [1]. Traditional silicon-based power semiconductor devices have reached their operational limits in high-power and high-frequency applications, necessitating the adoption of wide bandgap (WBG) materials such as Silicon Carbide (SiC) and Gallium Nitride (GaN) [2,3]. These advanced semiconductors offer superior electrical and thermal properties, enabling reduced switching losses, higher breakdown voltages, and enhanced thermal conductivity [4]. As a result, SiC and GaN power devices are widely adopted in renewable energy systems, electric vehicle (EV) charging stations, industrial motor drives, and high-voltage transmission networks [5]. The ability of these materials to operate at higher temperatures and switching frequencies compared to traditional silicon-based devices makes them ideal for next-generation power grids that require greater efficiency and resilience [6-8].

The integration of SiC and GaN power electronics with the IoT has further transformed modern energy systems by enabling real-time data exchange, remote monitoring, and intelligent control [9]. IoT-enabled sensors embedded within power converters, transformers, and distribution networks continuously collect operational data, allowing for real-time analysis and decision-making [10]. This connectivity enhances the efficiency of energy distribution, reduces losses, and facilitates predictive maintenance strategies that minimize downtime and operational costs. In addition, the deployment of AI in IoT-integrated power electronics enables advanced data analytics, fault detection, and self-healing mechanisms that ensure grid stability [11]. These intelligent capabilities allow smart grids to dynamically adapt to changing demand patterns, optimize power flow, and enhance system reliability [12].