Deep Learning Based PV Cell System Design

Case ID:

This technology focuses on enhancement of current photovoltaic (PV) cells to optimize the overall system output of solar electricity. This technology uses a microprocessor based algorithm to monitor PV output in real-time to assist in reconfiguration of PV filters, converters, and inverters parameters for optimal PV cell system performance.


Photovoltaic (PV) cells are products that make up the solar panel. The PV cells are critical component in the renewable solar energy products as they are the electrical component that generates electricity when exposed to photons, also known as sunlight. By focusing on enhancing on the performance, and output, of the PV cells, the overall solar panel system will be producing renewable energy at an optimal level. PV cells have filters, converters, and micro inverters that require parameter setting each day, which are dependent on the exposure of the sun each day. These parameters are set up manually and are very time consuming if each PV cell’s parameter is configured individually. There is a need to auto-configure the PV cells parameters based on PV cell system performance autonomously. This will ensure that the PV cell systems performance are working at optimal levels. 



  • Solar electricity generation
  • Microgrid
  • Renewable energy


  • Increased efficiency of the whole PV cell system performance
  • Time saving
  • System optimization
Patent Information:
Contact For More Information:
Lewis Humphreys
Licensing Manager, Eller College of Mngmt & OTT
The University of Arizona
Lead Inventor(s):
Janet Roveda
Siteng Chen