一个具有边缘功能的轻量级LSTM用于低压配电柜中电气接头的温度预测
Yuan Gui1,2, Chengdong Yin1, Ruoxi Liu1
1State Gird Beijing Electric Power Research Institute, Beijing 100036, China.
Sensors (Basel, Switzerland)
|November 27, 2025
概括
一个新的深度学习模型预测了低压柜中的电气关节过热. 这种智能温度监控为预测性维护提供了更安全,更有效的方法,防止设备故障和火灾.
科学领域:
- 电气工程 电气工程
- 人工智能的人工智能
- 预测性维护是指预测性维护.
背景情况:
- 低压配电柜中的关节过热是一个重要的安全隐患,导致绝缘故障和火灾.
- 目前的检查方法是反应性的,不能预测发展的问题.
- 早期检测温度异常对于防止灾难性故障至关重要.
研究的目的:
- 开发一种基于深度学习的方法,用于电气接头的短期温度预测.
- 为了实现智能温度监测和低压配电系统的预测性维护.
- 为实时分析创建一个高效,边缘部署的模型.
主要方法:
- 采集多源数据 (电压,电流,功率,温度) 使用自定义传感器和Raspberry Pi边缘节点.
- 开发和比较深度学习模型,包括ARIMA,GRU和LSTM,用于温度预测.
- 优化了一个轻量级的LSTM模型,用于边缘部署,平衡精度和计算效率.
主要成果:
- 与其他模型相比,LSTM模型显示出更高的预测准确度 (RMSE=0.26°C,MAE=0.21°C,MAPE=0.54%).
- 轻量级边缘部署的LSTM模型实现了可比的精度 (RMSE=0.27°C,MAE=0.21°C,MAPE=0.67%) 降低了延迟和内存使用.
- 该模型有效地预测了6小时的温度波动,并在各种机柜条件下保持稳定.
结论:
- 边缘启用轻量级LSTM模型为低压配电系统中智能温度监控提供了实用解决方案.
- 这种方法平衡了预测准确性,实时性能和预测性维护的效率.
- 开发的模型通过早期检测和预防联合过热问题来提高电气安全.
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