间接增压转换器的性能优化与ANN支持的可适应的分级扩展P&O基于MPPT用于太阳能发电应用
K Krishnaram1, T Suresh Padmanabhan2, Faisal Alsaif3
1Department of EEE, E.G.S. Pillay Engineering College, Nagapattinam, Tamil Nadu, 611002, India. krishnaramkresearch@gmail.com.
Scientific reports
|April 6, 2024
概括
本研究引入了两种新的混合最大功率点跟踪 (MPPT) 方法,即ANN-ASSPO和WCO-PO,以改善部分遮阳下的太阳能采集. 这些技术提高了跟踪效率,减少了振荡,使光伏发电更可靠.
科学领域:
- 可再生能源系统可再生能源系统
- 光伏发电是光伏发电的发电方式.
- 电力电子 电力电子 电力电子
背景情况:
- 太阳能是关键的可再生能源,光伏 (PV) 系统因其清洁可靠的发电而获得引力.
- 光伏系统的效率受到位置,阴影和气候等因素的影响,需要先进的电源管理.
- 现有的最大功率点跟踪 (MPPT) 方法与部分遮阳作斗争,导致振荡和效率降低.
研究的目的:
- 提出和评估新的混合MPPT技术,以提高太阳能采集效率.
- 为了应对在部分阴影条件下识别全球最大功率点 (GMPP) 的挑战.
- 为了提高跟踪效率,减少沉降时间,提高光伏系统的转换效率.
主要方法:
- 开发两种混合MPPT技术:支持人工神经网络的可适应阶段规模的扰乱和观察 (ANN-ASSPO) 和基于水循环优化的扰乱和观察 (WCO-PO).
- 利用人工神经网络 (ANN) 在ANN-ASSPO中确定动态辐射条件的最佳缩放因子.
- 在WCO-PO中使用水循环优化 (WCO) 来准确识别部分遮阳期间的峰值功率.
- 使用MATLAB/Simulink.进行模拟和性能比较.
主要成果:
- 与传统方法相比,ANN-ASSPO和WCO-PO都在发电和输出电压方面表现出更好的表现.
- 提出的技术显示了平均沉时间的缩短和转换效率的提高,特别是在快速变化的辐射和部分阴影下.
- ANN-ASSPO有效地利用了最佳的缩放因子,而WCO-PO准确地确定了阴影下的峰值功率.
结论:
- 新型混合ANN-ASSPO和WCO-PO MPPT技术为在具有挑战性的条件下运行的光伏系统提供了显著的改进.
- 这些先进的MPPT策略提高了太阳能发电的可靠性和效率,有助于更广泛地采用可再生能源.
- 该研究验证了整合人工智能和优化算法的有效性,以实现强大的太阳能能源管理.
关键词:
人工神经网络支持可适应的阶段缩放扰动和观察 (ANN-ASSPO).电动汽车 (E-Vehicle) 是一种电动汽车.交联式增压转换器 (ILBC) 是一个最大功率点跟踪 (MPPT) 是指最大功率点的跟踪.太阳系 太阳系 太阳系 太阳系更多相关视频
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