电力发电机内部,冠状和表面部分放电相位分辨率图像的数据集
Juan David Zorrilla Henao1, Alejandro Segura1, Alejandro Tenorio1
1School of Electrical and Electronic Engineering (IEEE), Faculty of Engineering, Universidad del Valle.
Data in brief
|January 31, 2024
概括
这项研究收集了电力发电机部分放电的高分辨率PNG图像,用于分类. 这些数据支持开发用于自动检测和分析发电机故障的软件.
科学领域:
- 电气工程 电气工程
- 信号处理 信号处理
- 模式识别 模式识别
背景情况:
- 发电机中的部分放电 (PD) 存在重大风险.
- 对PD类型的准确分类对于有效的监测和维护至关重要.
- 现有的方法可能缺乏用于高级分类算法的强大数据集.
研究的目的:
- 为发电机监控提供部分放电图像的全面数据集.
- 为了促进自动化PD分类软件的开发和验证.
- 为电气系统中先进的模式分析奠定基础.
主要方法:
- 在哥伦比亚各地对40多个发电机的现场测量.
- 使用了来自Omicron Energy的部分放电模拟器.
- 收集了高分辨率PNG图像,重点关注相位解析图案 (PRPD) 和RGB颜色表示.
主要成果:
- 一个精心策划的PNG图像数据集,详细介绍了内部,表面和冠状泄漏.
- 确保图像的准确性,连贯性和高分辨率,以便进行可靠的分析.
- 证明了与物理现象相关联PRPD模式的潜力.
结论:
- 收集的数据集对于培训和验证PD分类算法非常有价值.
- 这些数据可以显著提升发电机中PD的自动检测和分析.
- 适用于需要复杂信号和模式分析的更广泛领域.
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