基于剩余LSTM的偏振电流的短期预测,以有效评估变压器绝缘
Aniket Vatsa1, Ananda Shankar Hati1, Prashant Kumar2
1Department of Electrical Engineering, Indian Institute of Technology (Indian School of Mines), Dhanbad, 826004, India.
Scientific reports
|January 16, 2024
概括
深度学习可以在短时间内预测变压器极化电流,减少测量时间. 一个新的残留LSTM模型显示出高精度和一致性,用于主动的变压器绝缘维护.
科学领域:
- 电气工程 电气工程
- 材料科学 材料科学 材料科学
背景情况:
- 极化和脱极化电流 (PDC) 的测量对于变压器绝缘分析至关重要.
- 传统的PDC测量是耗时的,容易导致数据损坏.
- 精确的绝缘敏感参数对于变压器建模和维护至关重要.
研究的目的:
- 开发一种基于深度学习的短期技术,用于预测极化电流.
- 为了解决耗时的传统PDC测量的局限性.
- 为了提高变压器绝缘状况评估的效率和可靠性.
主要方法:
- 使用剩余长短期内存 (LSTM) 网络,并使用空间快捷方式进行梯度传播.
- 将拟议的剩余LSTM模型与传统的LSTM,注意力LSTM,门式循环单元 (GRU) 和卷积神经网络 (CNN) 模型进行了比较.
- 雇员蒙特卡洛学,因为不确定性估计在两极分化当前预测.
主要成果:
- 拟议的剩余LSTM网络在极化电流预测中显示了最低的误差指标.
- 该模型在整个测试期间保持了预测一致性.
- 实现了PDC测量时间的显著减少.
结论:
- 开发的深度学习方法有效地预测极化电流,减少测量时间.
- 剩余LSTM模型提供了一种可靠和高效的方法来评估变压器绝缘条件.
- 这种技术使变压器能够采取积极的维护策略.
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