Generation Fuel Cost and Loss Minimization Using Salp Swarm Algorithm Based Optimal Power Flow

In this study, a nature inspired, intelligence swarm-motivated Salp Swarm Algorithm (SSA) has been used for solving non-linear, constrained and non-convex based optimal power flow (OPF) problem. The sailing and exploring characteristics of Salp in the seas exhibit swarming features that helps in finding the optimal solution. The prime objective of this paper is to optimize the generation cost and power loss by the proposed method while satisfying both equality and inequality constraints of the power system. The proffered method is a single objective approach that aims to minimize the selective objective such as fuel cost or losses of the system by meeting the load demand. In this work, the effectiveness of the Salp Swarm is employed as the main optimizer to adjust the control variables such as real power dispatch by optimizing the fitness function. Thus, the distinguished nature of swarming pattern of Salp and their exploring behavior, demonstrates the global optimal solution. The proffered method is coded using MATLAB software and its performance is validated for IEEE-30 bus system. The results accumulated under selective objective shows that the fuel cost and losses of the system are minimized compared to other well-known computational intelligence (CI) algorithms presented. Moreover, the proposed method reveals a substantial improvement in terms of global search capability and convergence attribute.