Maximum power tracking of wave power generation system based on improved particle swarm optimization

Abstract In a wave power generation system, traditional particle swarm optimization (PSO) is prone to premature convergence and local optimal solution when it used control the maximum tracking. Therefore, an improved PSO proposed, simulated annealing adaptive (SAAPSO) constructed. The algorithm controls inertia weight coefficient factor by hyperbolic tangent function makes nonlinear change. Using linear change strategy social learning factors self-learning factors, operation introduced, temperature set according initial state of population. population guided compare fitness value with random number in Metropolis criterion temperature, whether new generated judged, improve problem that falls into locally solution. simulation results show can effectively prevent system from falling point, energy capture ability significantly improved.