储水电的未知异常情况监测方法
Jun Lee1, Kiyoung Kim1, Hoon Sohn1
1Department of Civil and Environmental Engineering, Korea Advanced Institute of Science and Technology, Yuseong-gu, Daejeon 34141, Republic of Korea.
Sensors (Basel, Switzerland)
|July 29, 2023
概括
本研究引入了一种自动化方法,用于检测储水电 (PSH) 设施中的新异常情况. 该方法有效地识别和分类PSH问题,并具有很高的准确性,即使使用最小的数据.
科学领域:
- 储能系统 储能系统 储能系统
- 水力发电发电是水力发电的发电方式.
- 状态监测和故障检测检测和故障检测
背景情况:
- 储水电 (PSH) 设施以重力潜能形式储存能量.
- 监测PSH的状况,特别是其潜水和旋转的水力发电轮机,是复杂的.
- 现有的方法往往需要专家的干预来检测新的异常状况.
研究的目的:
- 开发一种自动化方法来检测PSH中的新奇异常状况.
- 为了使现有分类模型对新故障进行自动更新和优化.
- 减少在PSH状态监测中依赖专家知识的依赖.
主要方法:
- 实施一种新的PSH自动检测系统.
- 自动更新和优化异常状况分类模型.
- 使用来自现场PSH设施的传感器数据进行评估.
主要成果:
- 拟议的方法在检测新的异常PSH条件时达到85.89%的准确性.
- 检测新的条件需要不到三个数据点.
- 鉴定条件的平均分类准确率达到99.73%.
结论:
- 开发的自动化方法有效地检测和分类PSH中的新异常情况.
- 该系统表现出高精度和高效率,即使数据有限.
- 这种方法在PSH状态监测和维护方面取得了重大进展.
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