基于模拟退火粒子群算法的最大风能跟踪方法  

Maximum Power Point Tracking Algorithm Based on Simulated Annealing Particle Swarm Optimization for Wind Power Systems

作　　者：张瑞成 何丹阳 

机构地区：[1]华北理工大学电气工程学院,河北唐山063210

出　　处：《工业控制计算机》2018年第10期40-41,49,共3页Industrial Control Computer

基　　金：河北省自然科学基金资助项目(F2018209201)

摘　　要：针对风力发电系统在额定风速以下运行时的最大风能跟踪问题,为了提高风能利用效率,提出了一种基于模拟退火粒子群优化算法的最大风能跟踪方法。优化算法以粒子群算法为主体,使搜索快速逼近最优解,在局部寻优中引入模拟退火算法的机制,通过退火迭代过程,使搜索接受劣解的概率逐渐变小,最终趋向全局最优解。优化算法避免了陷入局部最优和早熟的情况,提高了搜索精度。通过Matlab/Simulink软件建立了5.5 kW双馈风电机组模型和组合风速仿真模型,分别利用常规粒子群算法和优化算法完成风力发电的最大风能跟踪控制,并进行了对比分析。A maximum wind energy tracking method based on the simulated annealing particle swarm optimization algorithm is proposed to improve the efficiency of wind energy utilization.The optimization algorithm based on the particle swarm algorithm makes the optimal solution be approached quickly in search.The mechanism of the simulated annealing algorithm is introduced into the local optimization.Through the iterative iteration process,the probability of searching and accepting the inferior solution gradually becomes smaller,and finally tends to the global optimum.Consequently,the situation of falling into a local optimum and precocity is avoided.

关 键 词：风力发电 最大风能跟踪 模拟退火 优化粒子群算法 

分 类 号：TM315[电气工程—电机] TP18[自动化与计算机技术—控制理论与控制工程]