Ain Shams Engineering Journal (Jun 2022)
Real power loss reduction by German shepherd dog, explore –save and line up search optimization algorithms
Abstract
In this work German Shepherd Dog Optimization (GSDO) Algorithm, Explore and Save (ES) algorithm, Line up Search Optimization (LSO) algorithm has been projected to solve optimal reactive power problem. Projected GSDO algorithm has been formulated based on the trained activity of the German shepherd dogs in searching operation along with human beings. The smelling properly of the German shepherd dogs has been very powerful through that they will explore in the searching area and their baking volume will determine the fitness value. Secondly in this work Explore and Save (ES) algorithm has been applied to solve optimal reactive power problem. Search operations conducted during disaster period has been emulated to model the algorithm. The direction of the exploration or search is done by randomly choosing one among Inkling’s. Then Line up Search Optimization (LSO) algorithm has been utilized to solve the optimal reactive power problem. Proposed LSO algorithm is modeled based on the human behavior in many places. In any transactions client service is depending on the quality of the client actions and service staff members. With and without considering voltage stability index evaluation proposed GSDO, ES, LSO algorithms is tested in IEEE 30, bus system. Then Proposed GSDO, ES, LSO algorithms has been tested in standard IEEE 14, 57, 118 and 300 bus test systems without considering the voltage stability index. Minimization of voltage deviation, voltage stability enhancement and power loss minimization has been achieved.
