An Artificial Intelligent Hybrid Controller for Solar and Battery Fed Five-Level UPQC for Power Quality Improvement

Mohan Babu Guguloth, G. Suresh Babu, E. Vidya Sagar

Abstract


This study examines, the diode clamped five-level unified Power quality conditioner coupled with photovoltaic and battery storage systems to handle the power quality  related problems. To eliminate the requirement of the complex transformations like abc, dq0, ?? , the artificial neural network based control scheme with levenberg- marquardt back propagation training method is adopted for the diode clamped 5L-UPQC to produce the necessary reference signals for the shunt and series voltage source converters. In addition, adaptive neuro-fuuzy interface system hybrid controller is suggested for DC link error current minimization which adapts both the properties of fuzzy and ANN. The prime goal of the developed scheme is to maintain constant DC link capacitor voltage during load variations, reduction of total harmonic distortion in the source current and load voltage waveforms, maximum elimination of source voltage distortions like sag/swell and disturbances. The suggested method was demonstrated on two cases with various combinations of loads. However, to exhibit the performance of the proposed technique, the comparison is carried out with the ANN, proportional integral controller and Sliding mode controller. 


Keywords


Renewable Energy, solar, Electrical engineering

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References


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DOI (PDF): https://doi.org/10.20508/ijrer.v14i1.14356.g8849

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