在电力物联网中识别配电变电站的拓,使用动态电压负载波动流量分析
Yongjin Xu1, Jifan Lv1, Jiaying Wang1
1State Grid Zhejiang Marketing Service Center, Hangzhou, China.
PeerJ. Computer science
|March 4, 2024
概括
本研究介绍了一种隐藏的马尔科夫模型 (HMM),用于在低压分布网络 (LVDN) 中准确识别电力线路拓. 改进的方法在维护和重新配置期间提高了电网稳定性和能量计量精度.
科学领域:
- 电气工程 电气工程
- 电力系统分析 分析 分析
- 网络拓学 网络拓学
背景情况:
- 电力线路的维护和重新配置对于电网稳定性和精确的能量计量至关重要.
- 现有的方法在保持电网稳定性和在电力线路运行期间的测量准确性方面面临挑战.
- 准确识别配电变电站拓对于有效的电网管理至关重要.
研究的目的:
- 在电力线维护和检查期间,解决电网稳定性和能源计量精度方面的挑战.
- 引入一种增强的方法来识别低压配电网 (LVDN) 变电站的拓.
- 开发LVDN变电站的拓识别模型,使用消耗数据和节能原则.
主要方法:
- 利用隐藏的马尔科夫模型 (HMM) 在LVDN变电站中进行拓识别.
- 开发了一个拓识别模型,利用时间序列的电力消耗数据和节能.
- 将拓识别问题转换为凸优化问题,使用HMM减少数据维度.
主要成果:
- 拟议的基于HMM的方法在50次代中实现了0.9的区分精度,最小的误差趋同.
- 与替代品相比,在能源效率 (0.89) 和负载平衡 (0.85) 中表现出优异的性能.
- 在精度 (0.9) 和计算时间 (35.1 ms) 之间取得了平衡,优于其他分析方法.
结论:
- 拟议的HMM方法有效地识别了LVDN变电站拓,提高了电网稳定性和能量计量精度.
- 这种方法为电力线路重组,维护和检查所带来的挑战提供了可靠的解决方案.
- 这些发现为行业提供了宝贵的见解,提高了电力系统的整体可靠性和运营效率.
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