光伏发电系统的最大功率点跟踪使用改进的量子行为粒子群集优化
Gwo-Ruey Yu1,2, Yong-Dong Chang3, Weng-Sheng Lee1
1Department of Electrical Engineering, National Chung Cheng University, Chia-Yi 62102, Taiwan.
Biomimetics (Basel, Switzerland)
|April 26, 2024
概括
改进的量子行为粒子群集优化 (IQPSO) 在光伏系统中增强了最大功率点跟踪 (MPPT). 与现有的算法相比,这种新方法提供了更高的准确性和速度,即使在部分阴影下也是如此.
科学领域:
- 电气工程 电气工程
- 可再生能源系统可再生能源系统
- 计算智能是一种计算智能.
背景情况:
- 光伏发电系统 (PGS) 需要高效的最大功率点跟踪 (MPPT) 来实现最佳的能源采集.
- 现有的MPPT算法,包括量子行为粒子集群优化 (QPSO),可能遭受过早的融合,限制跟踪精度和速度.
- 部分阴影条件 (PSC) 通过引入多个功率最大值,使MPPT显著复杂化.
研究的目的:
- 引入一个改进的量子行为粒子集群优化 (IQPSO) 算法,用于在PGS中增强MPPT.
- 解决QPSO固有的过早融合问题.
- 为了提高跟踪精度和减少在各种操作条件下的跟踪时间,包括PSCs.
主要方法:
- 开发IQPSO算法,包括对概率分布估计和指数衰减进行调整,以实现更快的趋同.
- 在MPPT系统中实现IQPSO,使用串行反推转换器.
- 通过单峰,多峰,辐射变化和全天实验进行实验验证.
主要成果:
- 与QPSO,火算法 (FA) 和粒子优化 (PSO) 相比,IQPSO显示出更高的跟踪准确性.
- IQPSO显著减少了跟踪时间,提高了整体的融合效率.
- 该算法在理想和部分阴影条件下有效地在最大功率点实现了最佳运行.
结论:
- 拟议的IQPSO算法为光伏系统中的MPPT提供了强大的和高效的解决方案.
- IQPSO克服了传统QPSO的局限性,在速度和准确性方面提供了更好的性能.
- 这些发现突显了IQPSO在提高太阳能发电可靠性和能效方面的潜力.
相关概念视频
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01:23Maximum Power Flow and Line Loadability
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01:11Power Factor
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01:17Ampere-Maxwell's Law: Problem-Solving
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