This reference design features an analog front end for DC arc detection in solar applications based on artificial intelligence (AI). DC arcing induces a high-frequency noise onto the DC sting current. To detect the arcing frequency, data is acquired and feed into an embedded AI model, which is trained to identify the arc. Compared to traditional arc detection approaches this achieves higher accuracy with less computational efforts. Besides the signal-chain for arc detection this design offers features for collecting and labeling arcing data for embedded AI model training. This hardware works with TMDSCNCD28P55X, controlCARD of C2000™ F28P55xdevices, as well as other C2000 controlCARDs in 180-pin connector and is part of a AI based arc detection tool chain, including different software tools for collecting arc data, training an embedded AI model and validating the system.
A fully assembled board has been developed for testing and performance validation only, and is not available for sale.
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Reference design overview and verified performance test data
Detailed schematic diagram for design layout and components
Complete listing of design components, reference designators, and manufacturers/part numbers
Detailed overview of design layout for component placement
Files used for 3D models or 2D drawings of IC components
Design file that contains information on physical board layer of design PCB
PCB layer plot file used for generating PCB design layout
Includes TI products in the design and potential alternatives.
| Type | Title | Date | ||
|---|---|---|---|---|
| Certificate | TIDA-010955 EU Declaration of Conformity (DoC) | Aug. 13, 2024 | ||
| Design guide | Analog Front End Reference Design for Machine Learning Arc Detection in Solar Applications | PDF | HTML | Aug. 12, 2024 |
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