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.