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Conducting a three-phase short circuit test on an unloaded synchronous machine helps understand its impact on the system. The AC fault current's oscillogram, with the DC offset removed, reveals that the waveform amplitude decreases from an initially high value to a steady-state level for one phase of the machine.
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通过从信号调节系统中提取特征来识别部分放电源.

Itaiara Felix Carvalho1, Edson Guedes da Costa1, Luiz Augusto Medeiros Martins Nobrega1

  • 1Department of Electrical Engineering, Federal University of Campina Grande, Aprigio Veloso 882, Universitário, Campina Grande 58429-900, Brazil.

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概括

本研究引入了一个新的系统,用于检测,分离和分类部分放电 (PD) 在变电站使用信号调节和机器学习. 该方法实现了高精度,使得PD监测更可靠,更具成本效益.

关键词:
部分排放的分类.部分放电部分放电信号调节系统的信号调节系统

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科学领域:

  • 电气工程 电气工程
  • 电力系统分析 分析 分析
  • 信号处理 信号处理

背景情况:

  • 部分排放 (PD) 对变电站设备完整性构成重大风险.
  • 有效地检测,分离和分类PD对于防止故障至关重要.
  • 现有的方法可能会面临采样要求和信号与噪声比的挑战.

研究的目的:

  • 开发一个有效的系统来检测,分离和分类变电站中的部分排放.
  • 为了减少采样要求并提高PD检测的信号噪声比.
  • 利用机器学习进行准确的PD源分离和分类.

主要方法:

  • 实施信号调节系统以减少高频组件 (高达50 MHz).
  • 机器学习算法的应用,包括K-means,高斯混合模型 (GMM),Mean-shift和支持向量机 (SVM),用于PD分析.
  • 从有条件信号中提取特征以进行分类.

主要成果:

  • 信号调节系统成功地减少了高频噪声,并改善了信号噪声比.
  • 不同部分放电源的有效分离可以在没有信息丢失的情况下实现.
  • 部分排放源的分类准确度达到了93%.

结论:

  • 建议的信号调节和机器学习方法为PD监控提供了有效的解决方案.
  • 该系统有助于更经济,可扩展和可靠的变电站监控.
  • 这些发现为电力系统中先进的PD诊断工具铺平了道路.