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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.
Sensors (Basel, Switzerland)
|April 13, 2024
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.
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.
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