@ARTICLE{Rudraram_Ramesh_A_2023,
 author={Rudraram, Ramesh and Chinnathambi, Sasi and Mani, Manikandan},
 volume={vol. 69},
 number={No 3},
 journal={International Journal of Electronics and Telecommunications},
 pages={605-613},
 howpublished={online},
 year={2023},
 publisher={Polish Academy of Sciences Committee of Electronics and Telecommunications},
 abstract={A Novel Intelligent control of a Unified Power Quality Conditioner (UPQC) coupled with Photovoltaic (PV) system is proposed in this work. The utilization of a Re-lift Luo converter in conjunction with a Cascaded Artificial Neural Network (ANN) Maximum Power Point Tracking (MPPT) method facilitates the optimization of power extraction from PV sources. UPQC is made up of a series and shunt Active Power Filter (APF), where the former compensates source side voltage quality issues and the latter compensates the load side current quality issues. The PV along with a series and shunt APFs of the UPQC are linked to a common dc-bus and for regulating a dc-bus voltage a fuzzy tuned Adaptive PI controller is employed. Moreover, a harmonics free reference current is generated with the aid of CNN assisted dq theory in case of the shunt APF. The results obtained from MATLAB simulation.},
 type={Article},
 title={A Novel Intelligent Neural Network Techniques of UPQC with Integrated Solar PV System for Power Quality Enhancement},
 URL={http://ochroma.man.poznan.pl/Content/128303/PDF/27-28-3867-Rudraram-sk.pdf},
 doi={10.24425/ijet.2023.146514},
 keywords={PV system, Re-lift Luo DC-DC converter, Cascaded ANN MPPT, Adaptive PI controller, CNN assisted dq theory},
}