基于变压器的深度学习网络用于在输电线路中检测故障,分类和位置预测
Bousaadia Baadji1, Soufiane Belagoune2, Sif Eddine Boudjellal3
1Institute of Electrical and Electronics Engineering, IGEE, (Ex: Inelec), University of M'hamed Bougara, Boumerdes, Algeria.
概括
一种新的层次深度学习方法 (HDLA) 改善了电力系统中的故障检测,分类和位置预测. 这种方法通过有效分析原始功率数据来提高稳定性和可靠性.
科学领域:
- 电气工程 电气工程
- 人工智能的人工智能
- 电力系统分析 分析 分析
背景情况:
- 保持电力系统的稳定性和可靠性至关重要.
- 准确的故障诊断对于减少经济损失和加强保护至关重要.
研究的目的:
- 引入一种新的层次深度学习方法 (HDLA),用于传输线路的高效故障诊断.
- 使用同步的原始电源数据进行故障检测 (FD),故障类型分类 (FTC) 和故障位置预测 (FLP).
主要方法:
- 使用基于变压器的两级架构进行分类和回归.
- 直接处理同步的原始三相电流和电压样本,绕过传统的特征提取.
- 开发了一种层次深度学习方法 (HDLA).
主要成果:
- 与现有的深度学习方法相比,实现了更高的性能.
- 在分类任务的准确性,回忆力,精度和F1分数方面显著改进.
- 展示了用于故障位置预测的降低平均绝对误差 (MAE) 和根平均平方误差 (RMSE).
结论:
- HDLA为电力传输线路的实时故障诊断提供了准确有效的解决方案.
- 该方法处理原始数据的能力降低了计算复杂性.
- 在各种故障场景中验证的有效性,包括不同的电阻,初始角度和噪声水平.
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