Chaotic-PSO Approach For Optimal Power Flow

Optimal power flow (OPF), which aims to find the safest and most economical operating point while satisfying a variety of constraints, is a significant optimization problem in power systems. In this paper, a novel approach to OPF problem solution with Chaotic Particle Swarm Optimization (CPSO) is presented. Suggested approach improves the algorithm’s capacity to break out of local optima and converge towards global solutions by fusing the chaotic dynamics with the exploration-exploitation powers of Particle Swarm Optimization (PSO). The particle movement becomes unpredictable due to its chaotic character, which enhances the algorithm’s capacity to negotiate difficult solution spaces. In comparison to conventional optimization methods, the research assesses the performance of CPSO-OPF algorithm on benchmark test systems. The suggested method finds nearly optimal solutions for OPF issues, balancing computer efficiency and solution quality, as demonstrated by simulation results. The convergence and robustness of CPSO-OPF render it a valuable tool for resolving complex and large-scale power system problems, hence promoting the development of robust and effective energy management approaches.