DSP Implementation of a Novel Recurrent Neural Network Controller into a TI Solar Microinverter

Wanjau Waithaka, Abdullah Al Hadi, Shuhui Li, Xingang Fu, Rajab Challoo

Research output: Contribution to conferencePaper

6 Scopus Citations


Single-phase grid-tied inverters are widely used to integrate small-scale renewable energy sources and distributed generations to the utility grid. A novel Recurrent Neural Network (RNN) current controller is introduced to solve the resonant problem associated with LCL based inverters. The well-trained RNN controller was validated through a Texas Instruments (TI) LCL filter based solar microinverter kit that contains a C2000 TI microcontroller. The inverter closed current loop test without the grid connection was conducted in a laboratory setup to verify the RNN contoller functionality. The laboratory waveform results verified that the RNN current controller produced the strong tracking of the measured inverter AC current to its reference value even at a low sampling frequency of 5KHz. The proposed RNN current controller also proved to perform better than the built-in three-pole-three-zero (3P3Z) current controller provided by TI and produce lower values of Total Harmonic Distortion (THD).
Original languageAmerican English
Number of pages5
StatePublished - 2021
Event2021 IEEE Power & Energy Society General Meeting (PESGM) - Washington, D.C.
Duration: 26 Jul 202129 Jul 2021


Conference2021 IEEE Power & Energy Society General Meeting (PESGM)
CityWashington, D.C.

NREL Publication Number

  • NREL/CP-5D00-82304


  • digital signal processor
  • microinverter
  • recurrent neural network
  • solar integration
  • total harmonic distortion


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