Optimal Power Flow under Both Normal and Contingent Operation Using Hybrid Particle Swarm Optimization and Nelder Mead Algorithms (PSO-NM)

In this paper, solving optimal power flow problem has been investigated by using hybrid particle swarm optimization and Nelder Mead Algorithms. The goal of combining Nelder-Mead (NM) simplex method and particle swarm optimization (PSO) is to integrate their advantages and avoid their disadvantages. NM simplex method is a very efficient local search procedure but its convergence is extremely sensitive to the selected starting point. In addition, PSO belongs to the class of global search procedures but requires much computational effort. PSO-NM algorithm is proposed in order to solve the optimal power flow and find optimal settings of OPF control variables. The proposed method is examined on IEEE 30-bus standard test system by considering fuel cost minimization, voltage profile improvement, and voltage stability enhancement in normal and contingency conditions. It is also possible to apply the mentioned algorithm for optimal settings of OPF control variables considering non-smooth piecewise quadratic and non-convex cost functions. Simulation results are presented and compared with other intelligent algorithms in the previous literature. The results indicate that PSO-NM algorithm is effective in solving OPF problem.