基于物理学的神经网络,用于气体隔热开关设备振动系统中强大的等效阻尼参数反转和故障诊断
Yuhang Li1, Yuhui Lv1, Yingjie Yan1
1School of Electrical Engineering, Shanghai Jiao Tong University, Shanghai, 200240, China.
Scientific reports
|July 8, 2025
概括
这项研究引入了一种新的方法,将物理信息神经网络 (PINNs) 与实验数据相结合,以准确确定动力设备模拟的阻尼参数. 这种方法提高了关键基础设施中振动分析的可靠性和诊断能力.
科学领域:
- 工程 工程师 工程师 工程师
- 计算物理 计算物理
- 机器学习 机器学习
背景情况:
- 精确的振动信号模拟对于电力设备的故障检测至关重要,例如气体隔热开关设备 (GIS) 和变压器.
- 有限元法 (FEM) 需要精确的物理参数,例如阻尼系数,这些难以准确获得.
- 现有的参数估计实证方法往往耗时且缺乏精度.
研究的目的:
- 开发一种方法,使用物理信息神经网络 (PINNs) 和实验数据,准确地逆转振动系统中的缓冲参数.
- 为了提高动力设备的FEM模拟中的物理参数的准确性,稳定性和可解释性.
- 通过提高模拟准确度,提高动力设备的可靠性和诊断能力.
主要方法:
- 实现物理信息神经网络 (PINNs),将物理定律集成到神经网络架构中.
- 将PINNs与来自GIS等系统的实验振动数据相结合,用于参数反转.
- 通过与FEM和实验结果进行比较分析,验证该方法的准确性和耐噪性.
主要成果:
- 在没有噪音的情况下,FEM和实验结果之间具有高波形相似性 (振幅相似系数为0.869,正常化交叉相关性为0.926).
- 在1%的噪声水平下,低反转误差 (3%) 对于减噪参数.
- 证明了高达5%的噪声级别的良好的抗噪能力,表明了强度.
结论:
- 拟议的基于PINN的方法准确地逆转了减压参数,大大提高了动力设备的FEM模拟可靠性.
- 这种方法为参数估计的传统经验方法提供了更精确和可解释的替代方案.
- 这些发现为提高电力设备监控和维护的透明度和诊断能力铺平了道路.
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