Abstract：With the rapid development of artificial intelligence, embedded computing, and Internet of Things technologies, the intelligence of power electronics systems is gradually moving from conceptual exploration toward practical implementations. This paper systematically examines the potential, implementation pathways, and future application prospects of edge intelligence in the domain of power electronics, aiming to provide both a theoretical foundation and a practical framework for the development of new generation power electronics systems. This paper first examines the potential for edge intelligence in power electronics from the perspectives of the available data resources sampled naturally by the embedded sensors and the computational resources installed inside the equipment. On one hand, power electronics systems operate at high-frequency sampling, generating massive amounts of operational data that have yet to be fully analyzed or utilized. On the other hand, the digital signal processors (DSP) embedded in these equipment continuously upgrade in the past few decades, whose computational capability is far beyond the control consumption, in consequence, a large portion of computational resources remains underutilized while only real-time control tasks are executed. Therefore, the abundant data, coupled with redundant computational resources, provides the foundation for implementing edge intelligence in power electronics systems. Building on this foundation, the paper proposes three typical implementation pathways for edge intelligence of power electronics: (i) a lightweight pathway based on conventional processors, which leverages the residual computational and storage resources of DSPs to perform light tasks such as intelligent power conversion control and status monitoring; (ii) a computationally enhanced pathway based on external AI chips, embedding high-performance AI processors in power electronicsequipment to enable complex intelligent reasoning and decision-making while ensuring the real-time performance of high-frequency control; and (iii) a multi-equipment collaborative pathway based on the Internet of Things, which promotes the transition of edge intelligence from individual equipment to coordinated groups through equipment interconnection and resource sharing. Subsequently, three case studies—intelligent inverter control, DC arc detection, and intelligent completion of missing data—are presented to simply demonstrate the engineering feasibility of these pathways under different computational conditions and application scenarios. Furthermore, the paper presents an application-oriented perspective on edge intelligence across three levels. At the equipment level, it highlights the evolution from conventional control units to intelligent nodes endowed with autonomous sensing, collaborative decision-making, and security protection capabilities, thereby enabling intelligent functions of power electronics equipment. At the system level, the focus is on multi-equipment coordination and intelligent optimization to achieve system-wide adaptive control, efficient resource allocation, and enhanced overall operational performance, which could serve the intelligent operation of renewable generation station, smart factory, and etc. At the smart city level, it emphasizes the deployment of edge intelligence for real-time energy consumption monitoring, carbon emission management, and rapid emergency response, ultimately driving urban infrastructure toward greater intelligence, sustainability, resilience, and safety. Overall, this paper provides the theoretical framework, implementation pathways, and application value of edge intelligence in power electronics systems, offering significant potential for practical engineering applications. Edge intelligence in power electronics shares both similarities and distinctions with edge intelligence in other domains. Advances in this field could provide valuable insights for intelligent systems in other areas.
高峰. 电力电子边缘智能：潜力、路径及应用[J]. 电工技术学报, 2026, 41(3): 725-737.
Gao Feng. Edge Intelligence of Power Electronics: Potential, Route and Applications. Transactions of China Electrotechnical Society, 2026, 41(3): 725-737.
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2026, Vol. 41 
