通过时空图形神经网络对电力变压器的残余意识健康预测
1School of Intelligent Manufacturing, Yibin Vocational and Technical College, Yibin, Sichuan Province, China.
PloS one
|November 10, 2025
概括
这项研究引入了一种新的时空图神经网络 (STGNN),用于电力变压器健康监测. 该框架准确地预测变压器的健康状况,并使用SCADA数据检测故障,提高电力系统的可靠性.
科学领域:
- 电气工程 电气工程
- 人工智能的人工智能
- 数据科学数据科学数据科学
背景情况:
- 电力变压器故障会影响电网可靠性.
- 现有的监控系统难以处理复杂,动态的SCADA数据.
- 准确的健康预测和故障检测对于电网弹性至关重要.
研究的目的:
- 开发一个先进的框架,用于电力变压器健康状况预测和故障检测.
- 在多变量SCADA信号中建模动态拓依赖关系.
- 通过智能状态监控,提高电力系统的可靠性和弹性.
主要方法:
- 提出了一个残余意识的时空图神经网络 (STGNN) 框架.
- 时间变化的传感器图表是使用注意力机制构建的.
- 切比什夫光谱图卷积和有剩余连接的封闭循环单位 (GRU) 用于空间和时间建模.
- 一个健康指标 (HI) 是从预测余量来导出,用于通过基于量子的值检测异常.
主要成果:
- 该STGNN框架在合成SCADA数据上的预测准确度更高.
- 该方法在异常检测方面显著优于基线方法,特别是在杂,动态的条件下.
- 该模型有效地模拟了变压器子系统,包括绕线,核心,冷却,绝缘和龙头更换器.
结论:
- 拟议的STGNN为智能状态监控提供了一个可扩展,可解释和可部署的解决方案.
- 该框架增强了及时检测故障和准确预测电力变压器健康状况的能力.
- 该方法有助于提高现代电力系统的整体可靠性和弹性.
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