An effective power quality enhancement system for integrated photovoltaic cells utilizing cascaded ANFIS in a unified power quality conditioner

The arrival of power electronic devices for the control of loads has an effect on the Power Quality (PQ) at the utility grid’s distribution side. Meanwhile, PQ problems cause malfunctioning equipment, lost production time, loss of money for industry, inconvenience, and possible damage to household electrical appliances. Thus, the requirement for increased system efficiency is essential. Hence, this study proposes the control of a Unified Power Quality Conditioner (UPQC) in conjunction with a Photovoltaic (PV) system. Shunt and series converters attached back-to-back via a shared DC-link make up the PV-UPQC system. Subsequently, the Artificial Neural Network (ANN) controller reduces PQ problems and simplifies the control complexity. A Coupled quadratic Single Ended Primary Inductor Converter (SEPIC) connects the PV system to UPQC, and the Cascaded Adaptive Neuro Fuzzy Inference System- Maximum Power Point Tracking (ANFIS-MPPT) technique enables the optimization of power extraction from PV sources. The developed approach is implemented using the MATLAB/Simulink platform, and its performance is evaluated for Total Harmonic Distortion (THD), sag, and swell. The results show that the control maintains THD within the B-phase THD of 3.97% and R and Y phase THDs of 4.82% and 4.86%, and also obtained a voltage gain ratio of 1:15; the output levels increase substantially with reduced voltage stresses on the switching devices.