Enhanced Artificial Algae Algorithm based Distributed Generation Placement for Voltage Profile Enhancement

Power loss and voltage variability are main challenges in distribution systems. But, these difficulties are normally reduced by effectual network reconfiguration comprising the fusion of distributed generation (DG) units from the distribution network. Accordingly, the optimum size and placement of DGs are fundamental. Then, the network efficacy is reduced. This study was conducted to optimal place and size of DGs as a radial distribution network after and before reconfigurations. This study designs an Enhanced Artificial Algae Algorithm based Distributed Generation Placement for Voltage Profile Enhancement (EAAADG-VPE) technique. The presented EAAADG-VPE technique initially designs the EAAA by the integration of AAA with quasi-oppositional based learning (QOBL) concept. The presented EAAADG-VPE technique aims to regularize voltage, and minimizes power losses and penalty costs. For examining the real power system situation, the penalty factor was considered which roles a vital part in the minimized of power loss and increment of voltage profile. The performance validation of the EAAADG-VPE technique stated the enhanced performance over other recent state of art approaches.