基于二维卷积神经网络的故障检测和传输线路的分类,使用 Skalogram 图像
Pravati Nayak1, Shitya Ranjan Das1, Ranjan K Mallick2
1Department of Electrical Engineering, Siksha 'O' Anushandhan deemed to be University, Bhubaneswar, Odisha, PIN-751030, India.
Heliyon
|October 21, 2024
概括
本研究介绍了一种二维卷积神经网络 (2D-CNN),用于精确检测和分类电力传输线路的故障. 这种新的方法将故障信号转换为图像,即使在噪声和高电阻故障的情况下,也能达到高精度.
科学领域:
- 电气工程 电气工程
- 计算机科学 计算机科学
- 人工智能的人工智能
背景情况:
- 可靠的电力传输对于电网的稳定性和效率至关重要.
- 传输线路的快速故障检测可以防止级联故障,并最大限度地减少停机时间.
研究的目的:
- 开发一种使用二维卷积神经网络 (2D-CNN) 的传输线路故障检测和分类的新方法.
- 为了利用错误信号的 Skalogram 表示来增强特征提取.
主要方法:
- 故障信号被转换为二维扫描图像,用于输入到二维-CNN模型.
- 2D-CNN架构的设计是为了自动学习错误歧视的等级特征.
- 使用MATLAB/SIMULINK进行了广泛的模拟,使用10倍交叉验证来评估性能.
主要成果:
- 2D-CNN模型在理想的数据集上实现了99.9074%的准确性,用于12类故障分类.
- 该模型表现出对噪声的稳定性,在各种噪声水平 (20-40 dB) 中保持高精度 (99.629%至99.814%).
- 该模型还在高阻力故障条件下进行了验证,显示出一致的性能.
结论:
- 拟议的2D-CNN方法为输电线路故障分析提供了可扩展和高效的解决方案.
- 该方法有效地区分不同的故障类型,并在各种场景中很好地概括.
- 这项研究为将先进的机器学习整合到输电基础设施管理中铺平了道路.
相关概念视频
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