基于基于知识图的深度神经网络模型的智能电网设备的快速故障诊断
1The First Affiliated Hospital of Wenzhou Medical University, Wenzhou, Zhejiang, China.
PloS one
|February 14, 2025
概括
本研究介绍了一种使用深度学习和知识图的新智能电网设备故障诊断模型. 增强的YOLOv4模型提高了精度和速度,以实现高效的智能电网维护.
科学领域:
- 电气工程 电气工程
- 计算机科学 计算机科学
- 人工智能的人工智能
背景情况:
- 智能电网整合了先进的通信技术,以提高电力供应,但面临着设备故障风险的增加.
- 目前的智能电网故障诊断方法复杂且缺乏高精度,阻碍了维护效率.
- 及时检测故障对于智能电网的运行安全和可靠性至关重要.
研究的目的:
- 为智能电网设备开发一个先进的故障诊断模型.
- 为了提高智能电网维护的准确性,速度和易用性.
- 解决现有的复杂和低效率故障诊断技术的局限性.
主要方法:
- 使用了多式语义模型,结合了深度学习和知识图.
- 调整了YOLOv4目标检测网络架构.
- 综合知识图用于统一表征和存储多式联运信息.
主要成果:
- 与现有算法相比,拟议的基于YOLOv4的模型显示出更高的准确性和速度.
- 知识图的集成增强了故障诊断过程.
- 该模型被证明在智能电网维护方面更为用户友好.
结论:
- 开发的智能电网设备故障诊断模型比传统方法提供了显著的改进.
- 这种方法提高了电力供应的安全性,经济性和可靠性.
- 深度学习和知识图的融合为智能电网故障诊断提供了一个有希望的方向.
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