Improved Dual Fuzzy and ANN Controlled Three Level UPQC for Enhanced Power Quality

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.