Improved Dual Fuzzy and ANN Controlled Three Level UPQC for Enhanced Power Quality
Mrs.Shaik Rizwana1, Mr. Puvvula Nataraj Venkateshan2, Mr. Polagani Venkat Rakesh3, Mr. Vakiti Gopi Chand4, Mr. Yelamanchili Kushmanth Sai5
#1Assistant Professor in Department of EEE, Seshadri Rao Gudlavalleru Engineering College, #2#3#4 #5 B. Tech with Electrical and Electronics Engineering at Seshadri Rao Gudlavalleru Engineering College, Gudlavalleru.
Abstract-- Emerging trends and advances in techniques in power electronics, Unified Power Quality Conditioner (UPQC) has a superior performance compared to other methods. The paper proposes the application of a ANN based UPQC to enhance the power quality of a three-phase Low-voltage network connected to a hybrid distribution generation (DG) system. The proposed work emphases the detailed performance analysis of a distributed generation system that integrates a solar PV and wind energy system by utilizing Unified Power Quality Conditioner (UPQC) with an artificial neural network (ANN) controller with respect to proportional- integral (PI) controller. The core objective of the proposed ANN is to offer good steady and dynamic state performance compared to the PID controller. The system called UPQC-ANN-RE feeds energy generated by a photovoltaic array and a wind turbine into the electrical grid and loads attached to a system of 3-phase 4-wire electrical distribution. In addition to inserting active/real power in the utility grid, the system of UPQC-ANNRE functions as a UPQC, improving power quality signs e.g., voltage and current harmonics and power factor. A detailed analysis of the active-real power flow by converters is carried out to allow a good understanding of the operation of the UPQC-ANN-RE. The simulation outcomes are presented to assess the dynamic and steady state performance of the system of UPQC-ANN-RE connected to an electrical distribution system and to compare the consequences with the PI controller.
Index Terms: Distributed generation system, unified power quality conditioner, artificial neural network, power quality, renewable energy sources.
