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基于K-Medoids集群的电网数字操作数据集成方法,支持实时跨模式应用
Yuping Yan1, Hanyang Xie1, Liang Chen2
1Enterprise Architecture and Digitalization Department, Guangdong Power Grid Co., Ltd., Guangzhou, China.
Big data
|December 10, 2025
概括
本研究介绍了一种K-medoids集群方法,用于整合多源电网数据,提高效率,并使数字电网运营中的异常检测更快.
科学领域:
- 电气工程 电气工程
- 数据科学数据科学数据科学
- 计算机科学 计算机科学
背景情况:
- 电网数字运营面临多源异质数据的挑战,导致集成效率低下,异常检测速度缓慢.
- 现有的方法很难有效地处理和分析来自各种传感器的各种数据流.
研究的目的:
- 提出一个高效的数据集成方法,用于电网数字操作,使用K-medoids集群.
- 为了提高智能电网环境中异常检测的速度和准确性.
主要方法:
- 使用现场可编程门阵列 (FPGA) 并行架构,用于毫秒级同步获取和预处理多源数据 (振动,部分放电,温度).
- 实现了一个K-medoids集群算法,在应用层中使用密度加权的欧几里德距离度量和自适应的中心点选择.
- 开发了一个云服务层用于数据过,分析和访问,确保从基本到应用层的无数据流.
主要成果:
- 从各种电网数据源获得超过110MB/s的数据吞吐量.
- 在集成数据集中达到超过0.91的轮系数,这表明集群性能和数据可靠性很高.
- 证明有效地获取和整合多源异质电网数据.
结论:
- 拟议的方法显著提高了多源数据的聚类性能,使得异常的快速检测.
- 该架构支持实时处理,可以扩展到跨模式场景,改善电网运行和维护管理.
- 这种方法为智能电网运营的及时决策奠定了基础.
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