变压器绕热点温度的反转方法基于门式反复单元和自我注意和温度滞后.
Yuefeng Hao1, Zhanlong Zhang1, Xueli Liu1,2
1School of Electrical Engineering, Chongqing University, Chongqing 400044, China.
Sensors (Basel, Switzerland)
|July 27, 2024
概括
准确预测变压器绕热点温度对于故障预测至关重要. 本研究介绍了一种自注意门反复单元 (SA-GRU) 模型,该模型可以考虑数据滞后,提高反转精度并降低模型复杂性.
科学领域:
- 电气工程 电气工程
- 人工智能的人工智能
- 材料科学 材料科学 材料科学
背景情况:
- 变压器绕热点温度是绝缘性能和变压器健康状况的关键指标.
- 现有的数据驱动模型经常忽视温度数据的时间滞后,导致不准确的热点温度逆转.
- 准确的热点温度预测对于及时诊断变压器故障和运行安全至关重要.
研究的目的:
- 提出一种新的方法,用于精确逆转变压器绕热点温度,使用自我注意门反复单元 (SA-GRU) 模型.
- 为了研究考虑数据滞后对热点温度反转的准确性的影响.
- 优化特征选择过程,以提高模型性能和降低复杂性.
主要方法:
- 进行温度上升实验,从各种变压器组件和环境条件中收集全面的温度数据.
- 综合实验数据,明确考虑时间滞后,以生成候选输入特征.
- 采用基于相互信息 (MI) 的特征选择算法来确定输入特征的最佳子集.
- 开发了一种SA-GRU模型,通过将自我注意机制与门反复单元集成,以捕捉复杂的时间模式.
主要成果:
- 拟议的SA-GRU模型,包括数据滞后,在逆变压器绕热点温度方面,与经典模型相比,显示出更高的准确性.
- 功能选择有效地减少了冗余输入,从而产生了更简单,更有效的模型.
- 该模型准确地捕获了热点温度变化的动态趋势.
结论:
- 考虑到温度测量中的数据滞后,可以显著提高变压器热点温度反转的准确性.
- SA-GRU模型为预测变压器绕热点温度提供了强大而准确的解决方案,提高了故障预测能力.
- 这种方法为确保变压器可靠性和运行效率提供了有价值的工具.
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