关于在光伏阵列的MPPT中应用精细化层次人口算法的研究
Wei Liming1, Kou Qi1
1School of Electrical and Computer Engineering, Jilin Jianzhu University, Changchun, Jilin, China.
PloS one
|February 18, 2026
概括
本研究介绍了用于光伏最大功率点跟踪 (MPPT) 的改进层次人口算法 (PSSSA). PSSSA提高了跟踪速度和稳定性,优于PSO和SSA等现有方法.
科学领域:
- 可再生能源系统可再生能源系统
- 控制工程 控制工程 控制工程
- 优化算法 优化算法
背景情况:
- 太阳能 (PV) 系统在最大功率点跟踪 (MPPT) 中面临挑战,原因是缓慢的融合和不稳定的传输能力.
- 现有的算法,如粒子集群优化 (PSO) 和搜索算法 (SSA),可能遭受过早的融合和有限的效率.
研究的目的:
- 提出一种新的MPPT控制策略,即改进的层次人口算法 (PSSSA),以解决现有方法的局限性.
- 为了提高MPPT算法的搜索效率,融合速度和跟踪稳定性.
主要方法:
- PSSSA结合了SSA和PSO种群的细层次序列重建,并结合了PSO种群暂停机制,以防止过早的融合.
- 一个细层次的序列产生一个均分布的初始群体,使鸟和粒子群群体之间的合作优化.
- 种群分解,个体优化和层 wise 位置重组增强了协作和种群多样性,以提高搜索效率.
主要成果:
- 在Matlab中进行的模拟表明,PSSSA可以在0.2秒内准确地跟踪最大功率点,即使在照明的快速变化.
- 与PSO,SSA和扰乱观察 (P&O) 算法相比,PSSSA表现出明显更好的跟踪稳定性.
- 实现的最大功率点大约比使用PSO算法获得的功率点大64%,证实了增强的性能.
结论:
- PSSSA为光伏系统中的MPPT提供了强大的和高效的解决方案,克服了传统算法的局限性.
- 拟议的算法确保了跟踪效果的稳定性和速度,使其适合动态环境条件.
- 在融合速度,跟踪精度和光伏功率优化整体效率方面,PSSSA表现出卓越的性能.
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