图表神经网络用于故障诊断在光伏集成分布网络的弱点特征.
Junhao Liu1, Yuteng Huang2, Ke Chen2
1School of Microelectronics and Communication Engineering, Chongqing University, Chongqing 400044, China.
Sensors (Basel, Switzerland)
|September 27, 2025
概括
一个新的动态,自适应和合的双字段编码图形神经网络 (DACDFE-GNN) 改善了电力系统故障诊断. 该模型有效处理噪音和低训练数据,提高电网可靠性.
科学领域:
- 电气工程 电气工程
- 电力系统分析 分析 分析
- 人工智能在能源中的作用
背景情况:
- 有效的电力系统故障诊断对于可靠性至关重要.
- 新能源整合导致双向电力流,挑战传统方法.
- 数据驱动的方法需要大量高质量的训练数据,并与噪音和可变条件作斗争.
研究的目的:
- 为配电网络开发一个先进的故障诊断模型.
- 克服传统和现有的数据驱动故障检测方法的局限性.
- 通过减少训练样本和增加稳定性来提高故障检测性能.
主要方法:
- 引入一个动态聚合模块,用于降低噪音和信息集成.
- 关于合双场编码模块的建议,用于编码拓学和物理电气领域信息.
- 利用图形神经网络 (GNN) 来进行特征提取和传播学习.
主要成果:
- 拟议的DACDFE-GNN模型显示出卓越的故障检测性能.
- 该模型显示出显著的有效性,即使培训样本的比例较低.
- 对IEEE 34节点和IEEE 123节点料系统的实验验证证证了性能提升.
结论:
- DACDFE-GNN模型为发电网故障诊断提供了一个强大的解决方案.
- 该模型能够处理噪音和有限的数据,这使得它适用于现实世界的应用.
- 这种方法通过提高故障检测准确性和效率来提高电力系统的可靠性.
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