人工神经网络分析用于对有缺陷的高压陶绝缘体进行分类
1Electrical Metrology Division, National Institute of Standards (NIS), Giza, Egypt. ahmed.haiba@nis.sci.eg.
Scientific reports
|January 17, 2024
概括
声辐射技术有效地检测陶绝缘体中的部分放电 (PD). 人工神经网络在分类缺陷方面实现了96.03%的准确性,确保了电气系统的可靠性.
科学领域:
- 电气工程 电气工程
- 材料科学 材料科学 材料科学
- 状态监控 状态监控
背景情况:
- 陶绝缘体中的部分放电 (PD) 会导致退化,影响电气系统的安全性.
- 确定PD源,如制造缺陷和环境因素至关重要.
- 需要可靠的检测方法来保持绝缘体的完整性.
研究的目的:
- 在有缺陷的陶绝缘体中引入用于PD检测的声辐射技术.
- 开发一个智能分类器,用于准确的缺陷分类.
- 评估信号处理和分类方法的性能.
主要方法:
- 使用声波发射技术捕获PD信号.
- 应用数据处理技术,包括离散波波变换 (DWT) 和快速里埃变换 (FFT).
- 使用人工神经网络 (ANN) 来进行缺陷分类,并通过SVM和KNN验证.
主要成果:
- 通过使用DWT分析,ANN实现了96.03%的识别率.
- 通过FT分析,ANN实现了88.65%的认可率.
- 在对有缺陷的陶绝缘体进行分类时,证明了高准确度.
结论:
- 声辐射技术是PD检测和状态监测的可行方法.
- ANN提供了陶绝缘体缺陷的准确和有效的分类.
- 该研究强调了先进的信号处理对于可靠的电气绝缘监控的重要性.
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