Optimizing Harmonic Mitigation for Smooth Integration of Renewable Energy: A Novel Approach Using Atomic Orbital Search and Feedback Artificial Tree Control

This paper proposes an intelligent hybrid method for reducing harmonics to enhance power quality in a distribution system based on renewable energy sources. The proposed intelligent method, namely, the AOS-FAT technique, consolidates atomic orbital search (AOS) and a feedback artificial tree (FAT). The main objective of the proposed approach is to improve the quality of power by mitigating the harmonics. The AOS method is used to find the best values for basic and harmonic loop settings, like the shunt active power filter's direct current, voltage and the voltage at the terminals. Based on the change in load and PV parameters, a dataset variation is generated based on the objective function for minimum error. The optimal control signals are then generated using the FAT approach, which predicts the optimal parameters from the accomplished datasets. The proposed approach mitigates the overall harmonic distortion through the switching control pulses to enhance power quality. The control method concentrates on improving the maximum PV power when there is harmonic distortion by inserting the exact compensation current via the hybrid shunt active power filter. The proposed approach is implemented in MATLAB, and its performance is examined by comparing to existing methods. From the simulation outcome, the maximum PV power is 12 kW, and the THD is 1.1%.