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域自适应变压器部分放电识别方法结合AlexNet-KAN与DANN

Jianfeng Niu¹, Yongli Zhu¹

¹School of Electrical and Electronic Engineering, North China Electric Power University, Baoding 071003, China.

Sensors (Basel, Switzerland)

|April 28, 2025

概括

本研究引入了一种新的域适应方法,用于功率变压器部分放电识别. 该方法通过适应新的数据分布来提高模型性能,提高未标记或最小标记数据的检测准确性.

科学领域:

电气工程电气工程
人工智能的人工智能
机器学习机器学习

背景情况:

功率变压器的运行条件可以改变部分放电 (PD) 数据分布,导致未标记的新数据.
这种数据转移降低了现有的PD检测模型,降低了分类性能.
准确的PD识别对于变压器健康监测和防止故障至关重要.

研究的目的:

在不断变化的操作条件下开发一个域适应方法来识别变压器部分放电数据.
为了提高PD检测模型的准确性和稳定性,当面对未标记或鲜为标记的新数据时.
为了应对PD数据分布转移的挑战.

主要方法:

提出了一个新的AlexNet-KAN模型,集成Kolmogorov-Arnold网络 (KAN) 来增强AlexNet架构,以提高PD识别精度.
应用域对抗神经网络 (DANN) 从域适应理论到PD识别域.
开发了一个域适应型变压器PD识别模型,将AlexNet-KAN与DANNs结合起来.

主要成果:

拟议的AlexNet-KAN模型在变压器部分放电识别方面表现出更好的准确性.
域自适应模型有效地将PD数据从源域调整到目标域.
成功解决了变压器PD数据中的分配转移问题,即使新标签有限或不存在.

结论:

拟议的域适应式变压器部分放电识别方法有效地处理PD数据中的分布转移.
亚历克斯Net-KAN和DANN的集成提供了一个强大的解决方案,用于识别PD与未标记或很少标记的新数据.
这种方法显著提高了PD检测模型在动态功率变压器环境中的可靠性.

关键词:

科尔摩戈罗夫-阿诺尔德网络是深度学习是一种深度学习.域名适应域名适应域对抗性神经网络的领域.变压器部分放电

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