联合基于学习的绝缘体故障检测用于数据隐私保护
Zhirong Luan1, Yujun Lai1, Zhicong Xu1
1School of Electrical Engineering, Xi'an University of Technology, Xi'an 710048, China.
Sensors (Basel, Switzerland)
|July 8, 2023
概括
本研究介绍了一种保护隐私的方法,用于使用联合学习检测绝缘器故障. 这种方法可以确保在配电网络中准确检测故障,同时保护敏感数据.
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:
- 绝缘体是发电网中的关键组件,它们的故障会破坏运行.
- 手动故障检测效率低下,容易出现错误.
- 视觉传感器提供自动检测,但集中处理引发了隐私问题.
研究的目的:
- 提出使用联合学习的保护隐私的绝缘体故障检测方法.
- 解决基于视觉的绝缘体故障检测中集中数据处理的局限性.
- 为了保持高的检测准确度,同时保护数据隐私.
主要方法:
- 开发了一个用于绝缘体故障检测的联合学习框架.
- 构建了一个专门的绝缘器故障检测数据集.
- 在联合学习环境中训练有素的卷积神经网络 (CNN) 和多层感知器 (MLP) 模型.
主要成果:
- 拟议的联合学习方法实现绝缘器异常检测准确度超过90%.
- 该方法有效地保护模型培训期间的数据隐私.
- 实验结果验证了框架的适用性和保护隐私的能力.
结论:
- 联合学习为保护隐私的绝缘器故障检测提供了有效的解决方案.
- 拟议的方法平衡了高检测准确度与强大的数据安全性.
- 这种方法提高了发电网的可靠性和安全性.
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