Performance of Interline Unified Power Quality Conditioner (IUPQC) With PI, Fuzzy and ANFIS Controllers
A Navya1, A Panduranga Rao2, L Shanmukha Rao3

1A Navya, Studying IV, B.Tech, EEE, Kallam Haranadhareddy Institute of Engineering & Technology, Guntur, A.P., India.
2A Panduranga Rao, Assistant Professor in EEE department at Kallam Haranadhareddy Institute of Engineering & Technology, Guntur, A.P., India.
3L Shanmukha Rao, Professor & HOD of EEE at Kallam Haranadhareddy Institute of Engineering & Technology, Guntur, A.P., India.
Manuscript received on January 26, 2020. | Revised Manuscript received on February 05, 2020. | Manuscript published on February 30, 2020. | PP: 2566-2574 | Volume-9 Issue-3, February 2020. | Retrieval Number: C5497029320/2020©BEIESP | DOI: 10.35940/ijeat.C5497.029320
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Abstract: Several artificial intelligent control schemes are highly used in several applications, in that ANFIS controller has been greatly recognized due to enhanced performance over the classical PI and Fuzzy controllers. At present the multi-feeder power distribution system is deteriorated with continuity of supply and poor power quality standards. In this multi-feeder distribution system, it is a regular consumer related issue which is acquired due to malfunctioning of massive non-linear loads. These loads create the voltage or current imperfections on distribution networks which disrupts the power quality of distribution system. An efficient and reliable active compensation scheme is used for attaining enhanced power quality features at PCC of multi-feeder distribution system with effective control functions. The Multi-Feeder Unified Power Quality Compensator (MF-UPQC) is optimal choice for attaining enhanced power quality features and it is a combined shunt or series compensator driven by common DC-link. This paper recommends the Adaptive Neuro-Fuzzy Intelligent Controller (ANFIS) based prediction technique for generation of optimal switching states to enhance performance of proposed MF-UPQC to compensate all voltage-current PQ imperfections. The performance of proposed MF-UPQC is verified by classical PI, Fuzzy and proposed ANFIS control functions by using MATLAB/SIMULINK tool and results are conferred with proper comparisons.
Keywords: Adaptive Neuro-Fuzzy Inference System, Hybrid-Fuzzy Logic Controller, Multi-Feeder Distribution System, Fuzzy-Logic Controller, Power Quality Improvement; PI Controller, Total Harmonic Distortion (THD).