An edge AI‐powered power converter hardware platform for practical end‐to‐end AI‐to‐converter closed‐loop deployment
Abstract Modern power systems require increasingly flexible and responsive power conversion, driving the need for power converters with enhanced controllability and adaptability, where artificial intelligence (AI) plays a key role in enabling intelligent decision making, adaptive control and system level optimization. However, current AI applications in power systems remain largely restricted to cloud‐based post processing or device‐level demonstrations, lacking end‐to‐end hardware platforms that support the full pipeline from programming to deployment within a closed loop formed by physical converter hardware and AI modules. To address this gap, this paper proposes an edge AI‐powered power converter (EAI‐PC) hardware platform that utilizes locally deployed edge intelligence to establish bidirectional data exchange between converter low‐level control and EAI upper‐layer command dispatch. The platform features an end‐to‐end AI‐to‐converter closed‐loop deployment framework, enabling AI‐generated commands to be directly executed on physical converter hardware. By interconnecting converters (AC/DC and DC/DC) with an EAI module (using NVIDIA Jetson Orin Nano) via an industrial CAN protocol, this platform supports hardware‐level algorithm verification in the power system domain. The proposed EAI‐PC platform is experimentally evaluated across system configurations from single to multiple interconnected converters, assessing converter performance, command delivery and three AI deployment cases (two offline and one online): a multilayer perceptron (MLP) for power‐sharing prediction, a spatio‐temporal graph neural network (STGNN) for photovoltaic (PV) and load forecasting, and a bidirectional long short‐term memory (Bi‐LSTM) for online battery dispatch in load management and peak shaving.