一种新的混合数学深度学习技术,用于在复合绝缘体中提前警告闪光
Hamid Reza Sezavar1, Saeed Hasanzadeh2, Navid Fahimi3
1Qom University of Technology, Qom, Iran. sezavar@qut.ac.ir.
Scientific reports
|September 30, 2025
概括
这项研究引入了一种混合深度学习模型,用于预测污染复合材料绝缘体中的闪过. 这种新的方法准确地预测干带弧形和闪过,作为停电早期预警系统.
科学领域:
- 电气工程 电气工程
- 材料科学 材料科学 材料科学
- 人工智能的人工智能
背景情况:
- 污染的复合材料绝缘体容易发生闪电,导致停电.
- 准确预测闪过事件对于电网可靠性至关重要.
- 现有的方法在识别闪过前条件时往往缺乏准确性.
研究的目的:
- 开发一种新的混合数学深度学习模型,用于预测污染复合材料绝缘体中的闪过.
- 确定闪过前的关键条件,包括干带弧形 (DBA) 和闪过 (FO).
- 创建一个早期预警系统,以防止绝缘体故障和停电.
主要方法:
- 在泄漏电流 (LC) 数据上使用加权连续波量变换 (WCWT) 进行特征提取.
- 排放计数作为从WCWT分析中得出的性能指数.
- 使用与高斯安拟合参数集成的图形注意力网络 (GAT) 模型进行闪过预测.
主要成果:
- 拟议的WCWT方法在特征提取方面表现出优于典型的波纹分析的优势.
- 混合GAT模型有效地预测了干带弧形和闪过事件.
- 模型验证考虑了样本几何形状和污染水平等因素.
结论:
- 开发的混合深度学习模型为绝缘体闪过提供了可靠的早期预警工具.
- 该方法为在线监控应用提供了一个强大的方法.
- 这种进步有助于提高电力系统的稳定性和降低维护成本.
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