基于图形计算和由多源传感器收集的信息,在配电网络中恢复多目标电源供应
Jian Dang1, Shaopeng Zhang1, Yile Wang1
1College of Electrical Engineering, Xi'an University of Technology, Xi'an 710054, China.
Sensors (Basel, Switzerland)
|February 13, 2025
概括
这项研究利用智能传感器和图形模型增强复杂电网中的故障恢复. 新战略通过动态调整将整体恢复率提高了20%以上.
科学领域:
- 电气工程 电气工程
- 电力系统分析 分析 分析
- 网络优化 网络优化
背景情况:
- 配电网络日益复杂,需要提高故障恢复效率和可靠性.
- 传统传感器 (CT,PT) 和智能传感器 (D-PMU) 为故障分析提供多源数据.
- 图形计算模型为研究复杂的网络问题提供了一个框架.
研究的目的:
- 解决多目标配电网络中的故障恢复问题.
- 制定一个动态和适应性故障恢复策略.
- 提高电力恢复过程的效率和可靠性.
主要方法:
- 提出了电流计算模型和操作约束,可以适应图形计算框架内的拓变化.
- 利用最小跨越树理论来定义停电范围和恢复路径集.
- 配置智能传感器 (D-PMU) 用于全面收集故障信息,确保连接的负载顶部的覆盖.
- 建立了拓演变模型,以解释中断地区的重复转移,并探索恢复策略.
- 使用西省的分销网络验证了该模型.
主要成果:
- 拟议的战略通过包括单次和双重转移,停电地区的重复转移和负载减排在内的方法来动态调整恢复.
- 实验结果显示,整体故障恢复率显著提高,超过20%.
- 该模型有效地处理拓变化和复杂的中断场景.
结论:
- 开发的故障恢复策略提高了发电网的效率和可靠性.
- 智能传感器和图形理论的整合为动态故障恢复提供了一个强大的框架.
- 提出的方法为改善电网在日益复杂的情况下的弹性提供了一个实际的解决方案.
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