  An adaptive control approach for a three-phase grid-interfaced solar
photovoltaic system based on the new Neuro-Fuzzy Inference System with Rain
Optimization Algorithm (ANROA) methodology is proposed and discussed in this
manuscript. This method incorporates an Adaptive Neuro-fuzzy Inference System
(ANFIS) with a Rain Optimization Algorithm (ROA). The ANFIS controller has
excellent maximum tracking capability because it includes features of both
neural and fuzzy techniques. The ROA technique is in charge of controlling the
voltage source converter switching. Avoiding power quality problems including
voltage fluctuations, harmonics, and flickers as well as unbalanced loads and
reactive power usage is the major goal. Besides, the proposed method performs
at zero voltage regulation and unity power factor modes. The suggested control
approach has been modeled and simulated, and its performance has been assessed
using existing alternative methods. A statistical analysis of proposed and
existing techniques has been also presented and discussed. The results of the
simulations demonstrate that, when compared to alternative approaches, the
suggested strategy may properly and effectively identify the best global
solutions. Furthermore, the system's robustness has been studied by using
MATLAB/SIMULINK environment and experimentally by Field Programmable Gate
Arrays Controller (FPGA)-based Hardware-in-Loop (HLL).
