Comparison between particle swarm optimization and Cuckoo Search method for optimization in unbalanced active distribution system

Recently, the integration of distributed generation (DG) units to distribution networks has grown significantly. This integration provides an opportunity to control the power flow, resulting in the optimal power flow (OPF) at the distribution level. OPF can reduce system losses and decrease the DG generation costs. Additionally, it can improve the voltage profile. Applying OPF to distribution networks is a challenging task since the nature of distribution networks makes the OPF a nonlinear problem. In this paper, a multi-objective function is used to define the nonlinear power flow problem. To solve the OPF problem, the Particle Swarm Optimization (PSO) and Cuckoo Search (CS) algorithms are applied. These approaches are investigated utilizing IEEE 37 nodes test case. Comparing the results of the two methods shows that the CS algorithm performs better than PSO. The advantages of the CS algorithm, including fewer initial solutions, strong optimization searching ability, and fast convergence speed, make it an effective tool for solving the nonlinear optimization problem.