基于后向神经网络 (BNN) 的多层控制,以提高可变通信网络下的岛屿RES DC微电网的质量
Hira Anum1, Muntazim Abbas Hashmi1, Muhammad Umair Shahid2
1Institute of Mathematics, Khwaja Fareed University of Engineering & Information Technology (KFUEIT), Rahim Yar Khan, Pakistan.
Heliyon
|July 11, 2024
概括
一个新的落后神经网络 (BNN) 增强了面临通信问题的微电网 (MG) 的控制. 这种方法改善了多送系统中的负载共享和电压管理,优于传统方法.
科学领域:
- 电气工程 电气工程
- 电力系统 电力系统
- 人工智能的人工智能
背景情况:
- 微电网和能源社区涉及分布网络中的多个利益相关者.
- 农村配电网络遭受信息基础设施不足的困扰,造成了运营盲点.
- 传统的直流微电网控制方法容易受到通信延迟的影响.
研究的目的:
- 为应对多输送器微电网系统的负载共享和电压管理的优化挑战.
- 引入一个强大的控制方法,使用一个落后的神经网络 (BNN).
- 加强在配送网络中通信连接问题期间的控制.
主要方法:
- 落实一个落后的神经网络 (BNN) 来优化控制策略.
- 国家电力网计算分布式发电点的运行方向.
- 数学分析各种负载条件和不确定的线路特征在一个辐射式多料微电网网络.
主要成果:
- 拟议的BNN技术有效地管理负载分配和电压调节.
- BNN 显示了控制性能的提高,特别是在通信故障时.
- 这种方法确保了运营指令的计算,尽管通信链路的延迟.
结论:
- 逆向神经网络 (BNN) 是一种比传统方法更有效的控制技术,用于多料微电网.
- BNN提高了负载共享和电压管理的运行可靠性和精度.
- 拟议的技术可扩展到整个多源网络,解决基础设施的局限性.
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