部分与冠状病毒在XLPE覆盖导体中的排放:ML应用的高分辨率天线数据集
Ondřej Kabot1, Lukáš Klein2,3, Zdeněk Slanina1,4
1ENET centre - CEET, VSB - Technical University of Ostrava, Ostrava, Czech Republic.
Scientific data
|August 5, 2025
概括
本研究引入了一套高分辨率的数据集,用于区分电力系统中的部分放电 (PD) 和冠状放电. 该资源有助于开发机器学习模型,用于准确的故障诊断和改善基础设施安全.
科学领域:
- 电气工程 电气工程
- 材料科学 材料科学 材料科学
- 数据科学数据科学数据科学
背景情况:
- 准确区分部分放电 (PD) 和冠状放电对于电力系统诊断至关重要.
- 现有的机器学习 (ML) 模型开发受到PD和冠状病毒排放分类缺乏专门的,高可靠性数据集的阻碍.
研究的目的:
- 为PD和冠状病毒排放分类提供高分辨率数据集.
- 为了使ML模型用于动力系统诊断的开发和基准测试.
- 为了支持在非侵入性故障识别和绝缘风险减轻方面的进步.
主要方法:
- 使用非接触式双天线系统获取高分辨率数据集 (10^7样本/20毫秒).
- 控制的实验室条件模拟中压空中配电线路.
- 在两天内,收集了每个类别的100个标记测量,涵盖五种排放类型和两个背景条件.
主要成果:
- 一个新的,高保真数据集与PD和冠状病毒排放的实验性隔离信号类型.
- 数据集可以比较ML模型用于PD-冠状病毒分类.
- 促进研究轻量化模型,合成数据生成和噪声强度.
结论:
- 提出的数据集是推动电力系统基于机器学习的诊断的关键资源.
- 能够开发准确的,非侵入性的故障识别工具.
- 有助于提高诊断准确度,降低绝缘风险和提高电力基础设施安全.
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