Power Electronics Turing Test: A Path Toward Strong AI in Power Electronics

This paper presents a hypothetical Turing test in power electronics, leveraging structured computer vision as a step towards domain-specific artificial general intelligence (AGI). To illustrate the key principles of such a power electronics Turing test, we developed PowerVision, a computer vision framework designed to teach machines to understand schematic drawings. PowerVision comprises four key components: 1) ComponentNet: an image database for component recognition; 2) CircuitNet: an image database for schematic recognition; 3) NetlistMaker: a schematic recognition tool that converts human-readable schematics into netlists for SPICE simulations; and 4) NetlistClassifier: a circuit classification tool that can categorize different power electronics circuits based on machine-generated netlists. The PowerVision platform can facilitate the learning of power electronics fundamental principles by large-scale AGI models through human-accessible information including texts, schematics, computer simulations, and experimental results, ultimately enabling machines to comprehend power electronics.