在智能电网中使用NSCT和频段刻度图检测电力质量障碍和网络入侵
Pampa Sinha1, Kaushik Paul2, Snehalika Snehalika3
1KIIT University, Bhubaneswar, India. pampa.sinhafel@kiit.ac.in.
Scientific reports
|September 5, 2025
概括
这项研究引入了一个新的智能电网监控系统,使用非亚样本轮转换 (NSCT),SALSA和MCA以高精度检测电力质量干扰 (PQD) 和网络入侵,即使在噪音条件下.
科学领域:
- 电气工程
- 信号处理
- 电力系统的网络安全
背景情况:
- 智能电网需要对电力质量障碍 (PQD) 和网络入侵进行强有力的监控.
- 现有的方法往往难以应对复杂的信号异常和网络威胁.
研究的目的:
- 在智能电网中开发一个新的多尺度信号处理框架,用于同时检测PQD和网络入侵.
- 提高电力系统中异常检测的准确性和可解释性.
主要方法:
- 使用非亚样本轮转换 (NSCT) 具有适应权重,用于多尺度信号分解.
- 应用分割增强拉格朗的收缩算法 (SALSA) 进行稀疏优化和降低噪音.
- 使用形态组件分析 (MCA) 来隔离与故障相关的组件并提高可解释性.
- 集成的人工智能驱动的异常检测和弹性状态估计用于早期威胁标记.
主要成果:
- 实现了高检测精度:PQD的98.6%,网络入侵的97.2% (FDI,DoS,命令注入).
- 与基线方法相比显著改善,包括检测精度提高了14- 18%,错误阳性结果减少了22%.
- 对噪声 (高达30dB) 和数据损失 (高达20%) 的强度进行了展示.
- 创建可解释的时间频率图表以可视化独特的入侵特征.
结论:
- 拟议的NSCT-SALSA-MCA框架在智能电网监控方面取得了重大进展.
- 该系统提供准确,强大和可解释的电力质量问题和网络威胁的检测.
- 该框架显示了实时部署的潜力,增强了智能电网的安全性和运营弹性.
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