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- 2015
适用于光伏多峰功率跟踪的改进型粒子群优化算法
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Abstract:
针对在自然环境下光伏阵列上时常发生的局部阴影而引起P？？V曲线由单峰转变成多峰状态,从而导致常规最大功率跟踪算法失效的问题,在研究传统粒子群算法的基础上,提出了一种改进型控制算法。该算法采用全局模式和局部模式两种运行手段定位最大峰值点,在对粒子群优化的速度更新方式上,去除了大量的随机变量干扰,使结构优化非常明显。改进后粒子群优化算法能够使功率跟踪避免陷入局部最优,使之找到真正的最大功率点。通过与传统粒子群算法对比仿真及试验,结果表明,在光伏阵列局部遮荫的情况下,改进后的粒子群优化算法可以快速准确地搜索到最大功率点,追踪精度高达95％,并且比传统的粒子群算法在搜索效率上提升28％,较好地避免了陷入局部最优。
The partial shade on photovoltaic array appears repeatedly in natural environment to change single peak into multi？？peaks in P？？V curve. An improved algorithm following the traditional particle swarm optimization is proposed, where the global mode and local mode are adopted to locate the maximum power point. To accelerate the speed of particle swarm optimization, a lot of random and interfered variables are removed to realize an obvious structure optimization. The improved particle swarm optimization algorithm prevents power tracking from falling into the local optimum, and finds the true maximum power point. Simulation and test show that for partial shading, the improved algorithm can accurately and quickly search out the maximum power tracking point with 95％ tracking accuracy, and searching efficiency is 28％ higher than that of the traditional particle swarm algorithm. Especially, the local optimum can be avoided
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