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Updated: Jul 13, 2025

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智能电网异常点检测基于小化-最大化算法.

Lina Qiao1,2, Wengen Gao1,2, Yunfei Li1,2

  • 1College of Electrical Engineering, Anhui Polytechnic University, Wuhu 241000, China.

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|October 14, 2023
PubMed
概括
此摘要是机器生成的。

本研究引入了微小化-最大化 (MM) 算法,用于检测和定位电力系统中的异常值. 该MM算法有效地识别和确定电力系统异常,提高运行稳定性和数据准确性.

关键词:
在本地化,本地化.异常标志的检测异常标志的检测电力系统的动力系统.的小化最大化算法.

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科学领域:

  • 电气工程 电气工程
  • 数据科学数据科学数据科学
  • 控制系统 控制系统

背景情况:

  • 由于设备老化或传感器故障造成的电力系统中的异常值会影响数据质量和系统安全.
  • 准确的数据对于有效的电力系统监控,分析和控制至关重要.

研究的目的:

  • 开发和评估一种新的算法,用于检测和定位电力系统中的异常值.
  • 提高电力系统数据分析的可靠性和准确性.

主要方法:

  • 一个最小化-最大化 (MM) 算法被开发用于异常值的检测和定位.
  • 为高斯混合模型 (GMM) 集成未知参数的估计.
  • 在IEEE 14-bus系统上进行模拟,以测试算法的性能.

主要成果:

  • 与传统方法相比,MM算法在检测异常值方面表现优越.
  • 实现了异常值的准确定位,其概率超过95%.
  • 该算法有效处理异常值,提高数据完整性.

结论:

  • 拟议的MM算法为管理电力系统中的异常值提供了有效的解决方案.
  • 这种算法的实施可以增强电力系统的监控,分析和控制能力.
  • 这有助于确保电网的稳定可靠运行.