基于多源数据的变压器的智能故障诊断和运行状态监控,基于融合和采矿数据
Jingping Cui1, Wei Kuang2, Kai Geng3
1Ural International Institute of Rail Transit, Shandong Polytechnic, Jinan, 250104, China. cjp_sdp_edu@163.com.
Scientific reports
|March 4, 2025
概括
本研究介绍了使用多源数据融合和相关性分析的智能变压器故障诊断系统. 该模型准确地预测变压器的健康状况,并警告潜在的故障,确保电力系统的可靠性.
科学领域:
- 电气工程 电气工程
- 电力系统分析 分析 分析
- 在工程领域的人工智能.
背景情况:
- 变压器的可靠性对于高效的电力系统运行至关重要.
- 预测和健康管理 (PHM) 对变压器至关重要.
- 现有的故障诊断方法需要对复杂数据进行增强.
研究的目的:
- 为变压器提出一种新的智能故障诊断方法.
- 整合多源数据以提高诊断准确度.
- 为了实现早期预警,并防止灾难性的故障.
主要方法:
- 多源数据融合使用改进的权方法.
- 混合深度学习模型:双向LSTM,注意力机制,CNN.
- 状态评估的先验相关性分析.
主要成果:
- 准确预测负载率,温度和溶解气体指数.
- 在采样点88处识别异常条件 (如电弧放电).
- 实现了0.917的平均故障诊断率,平均平方误差为0.018.
结论:
- 拟议的模型有效地诊断变压器故障,并预测早期警告.
- 多源数据融合和混合深度学习提高了诊断性能.
- 该系统有助于电力系统的安全可靠运行.
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