随机卷积内核转换与实证模式分解用于电网绝缘体的分类
Anne Carolina Rodrigues Klaar1, Laio Oriel Seman2, Viviana Cocco Mariani3,4
1Graduate Program in Education, University of Planalto Catarinense, Lages 88509-900, Brazil.
这项研究通过将实证模式分解 (EMD) 方法与MiniRocket算法相结合,增强了绝缘体故障诊断. 这种方法显著提高了检测故障电绝缘体的准确性,提高了电力系统的可靠性.
科学领域:
- 电气工程 电气工程
- 信号处理 信号处理
- 机器学习 机器学习
背景情况:
- 可靠的电能供应取决于功能绝缘体.
- 来自绝缘体的超声波时间序列数据可以识别故障.
- 现有的方法需要改进,以准确地分类绝缘体故障.
研究的目的:
- 提出一种用于绝缘体故障诊断的新策略.
- 为了评估结合实证模式分解 (EMD) 方法与火箭算法的有效性.
- 为了提高电绝缘体故障检测的准确性和可靠性.
主要方法:
- 利用随机卷积内核转换 (火箭) 算法从时间序列超声数据中提取特征.
- 整合了三种EMD方法:用适应噪声 (CEEMDAN) 完成集体实证模式分解,实证波形转换 (EWT) 和变化模式分解 (VMD).
- 应用后勤回归分类器来使用提取的特征诊断绝缘体故障.
主要成果:
- 结合EMD方法和MiniRocket,显著提高了后勤回归的准确性.
- 实现了高诊断准确度:CEEMDAN的0.992,EWT的0.995和VDM的0.980.
- 与没有EMD的基线方法相比,证明了更高的性能.
结论:
- 经验模式分解方法,当与MiniRocket集成时,为绝缘体故障诊断提供了一种强大的方法.
- 拟议的战略提高了电力系统监控的准确性和可靠性.
- 这项研究突出了提高电气基础设施安全性和可靠性的潜力.
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