Power quality improvement by PV integrated UPQC using multi-level inverter with resilient back propagation neural network approach

Abstract

To increase the life and efficiency of power electronics equipment in a utility distribution system, the power quality improvement is essential part. In this work, to improve power quality by using Robust Resilient Back Propagation Neural Network (RBPNN) scheme for a Photovoltaic (PV)-Integrated Unified Power Quality Conditioner (UPQC) with cascaded multi-level inverter configurations are described. Among the proposed methods, there is no need to use a transformer and filter when multilevel UPQC is applied, and it is one of the great advantages. The proposed UQPC offers a PV array composition with a power converter connected to a DC-link capacitor that can compensate for voltage sag, swell, voltage interruption, harmonics and reactive power. The Robust Resilient Back Propagation Neural Network controller is generate gating pulses to the UPQC. The reference currents and voltages for the controller are estimated using Synchronous Reference Frame (SRF) theory. The proposed cascaded multi-level inverter-based UPQC is designed using Matlab/Simulink Software. The simulation results confirm that the proposed method gives good results compared with existing Adaptive neural Fuzzy Inference System (ANFIS) and fuzzy logic methods. A real-time hardware system is also established to validate the simulation results. The effectiveness of the proposed system RBPN-UPQC approach is compared for both simulation and experimental results gives better low THD level 1.22%.