基于模拟退火粒子群算法的波浪发电系统最大功率跟踪控制

波浪发电系统最大功率点跟踪控制中,传统粒子群算法存在早熟收敛和局部搜索能力不足问题,为此提出基于模拟退火算法的粒子群优化方案。该算法每次更新粒子的速度和位置时,通过比较当前温度下各个粒子的适配值与随机数的大小,从所有粒子中确定全局最优解的替代值,从而使粒子群算法在发生早熟收敛时能够跳出局部最优并快速找到全局最优解。仿真结果表明,与传统粒子群优化算法相比,模拟退火粒子群算法可有效避免波浪发电系统陷入局部最大功率点,并快速实现全局最大功率跟踪,提高了波浪能捕获率。

Maximum Power Point Tracking Algorithm Based on Simulated AnnealingParticle Swarm Optimization for Wave Power Systems

The particle swarm optimization (PSO) algorithm has low probability in searching global optimization and premature convergence in the maximum power point tracking (MPPT) control of the wave energy generation system. A novel simulated annealing particle swarm optimization (SAPSO) algorithm was proposed to solve the problem of the traditional PSO. When the speed and position of each particle were updated with SAPSO, the replacement value of the global maximum from all particles was confirmed by comparing the fitness of each particle of the current temperature and the random number value. As a result, the new algorithm could escape local maximum at the premature convergence and quickly discover global optimum solution. The simulation results showed that this novel algorithm could make the wave energy generation system effectively avoid the local optimization and fast achieve global MPPT control. The capture rate of wave energy was improved.