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The supply grid network has been periodically experiencing frequent problems with Power Quality (PQ), and these problems have gotten worse over time due to the rise of electrical appliances. Thus, supplying consumers with electricity in the custom of sinusoidal voltages and currents that have adequate magnitudes and frequencies near the common point of coupling (PCC) stays one of the Utility system's main responsibilities. Thus, in order to improve PQ in the supply grid network, this article examines the practice of solar-PV coordinated Unified Power Quality Conditioner (UPQC). By means of optimizing the utilization of solar energy and improving PQ, the Solar-PV fed UPQC contributes clean, renewable energy to the grid and solves environmental problems at the same time. This study presents Adaptive Leaky Least Mean Square (ALLMS) algorithm-based control techniques for UPQC, which include both traditional Proportional-Integral (PI) and Adaptive Neuro Fuzzy Inference System (ANFIS) controllers for UPQC series and shunt active converter switching. This method gets the reference signals with switching on the shunt and series voltage source converters (VSCs) of UPQC by means of iteratively updation of the weights. Accordingly, PQ consternations for instance, voltage sag and swell of load voltage and harmonic distortions of grid current are reduced when control techniques have been applied to solar-PV fed UPQC. This work is carried out in MATLAB/Simulation software, and also the results of simulations demonstrate that the suggested ANFIS controlled ALLMS algorithm is the most effective at improving power quality while adhering to IEEE-519 Standards when applied to a solar-PV fed UPQC.
Chilakapati et al. (Sat,) studied this question.