在光伏电池微电网中,适应性MPPT控制可用于在电网连接和岛屿运行之间可靠的过渡
U Siddaraj1, Udaykumar R Yaragatti2, Lakhshman Rao S Paragonda3
1Manipal Institute of Technology, Manipal Academy of Higher Education, Manipal, India. Siddaraj.u@manipal.edu.
Scientific reports
|February 6, 2026
概括
本研究介绍了一种混合人工神经网络 (ANN) 和粒子群优化 (PSO) 方法,用于在光伏电池微电网中进行最大功率点跟踪 (MPPT),从而改善能量提取和稳定性.
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
- 控制系统 控制系统
背景情况:
- 光伏 (PV) 系统需要高效的最大功率点跟踪 (MPPT) 来实现最佳的能量提取.
- 具有光伏和电池存储的微电网系统需要强大的控制来实现无电网连接和岛屿运行.
- 现有的MPPT技术在动态条件和准确性方面面临挑战.
研究的目的:
- 提出一种基于人工神经网络 (ANN) 和粒子集群优化 (PSO) 的新型混合 MPPT 控制器.
- 提高光伏电池微电网中的光伏能源提取效率和响应能力.
- 为了确保稳定的电力流和网联和岛屿模式之间的动态过渡.
主要方法:
- 在光伏电池微电网系统中集成ANN-PSO控制器.
- 在光伏阵列和电池储能系统 (BESS) 之间使用垂落控制的逆变器协调功率流.
- 在通用合点 (PCC) 监控电流和电压,以确保可靠的电力供应.
- 通过 MATLAB/Simulink 模拟,与 Perturb & Observe,ANFIS-PSO 和 PSO-SMC 技术进行比较.
主要成果:
- 与传统和智能MPPT方法相比,证明了优越的跟踪性能.
- 在不同辐射和负载条件下实现更快,更稳定的微电网运行.
- 验证了辐射率估计的更高准确性和更好的追踪响应性.
结论:
- 拟议的ANN-PSO MPPT方法显著提高了光伏能源整合效率.
- 该控制器促进智能,自主能源系统和弹性太阳能微电网.
- 这项研究有助于可持续发展目标7 (负担得起和清洁能源) 和可持续发展目标9 (工业,创新和基础设施).
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