在气体绝缘开关装置中进行部分放电诊断的子域适应囊网络.
Yanze Wu1, Jing Yan1, Zhuofan Xu1
1State Key Laboratory of Electrical Insulation and Power Equipment, Xi'an Jiaotong University, Xi'an 710049, China.
Entropy (Basel, Switzerland)
|May 27, 2023
概括
一个新的子域适应囊网络 (SACN) 在气体绝缘开关设备 (GIS) 现场数据中改进了部分放电 (PD) 诊断. 这种方法增强了特征表示,并适应了局部分布,达到93.75%的准确性.
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:
- 深度学习,特别是卷积神经网络 (CNN),在实验室环境中的气体绝缘开关设备 (GIS) 中显示出部分放电 (PD) 诊断的前景.
- 现场应用仍然存在挑战,因为CNN忽略了特征关系,需要大量的样本数据,阻碍了强大和高精度的诊断.
研究的目的:
- 开发一种用于在GIS现场环境中准确和可靠的PD诊断的新方法.
- 解决传统深度学习模型在处理现实世界现场数据复杂性的局限性.
主要方法:
- 在GIS中,一个子域适应囊网络 (SACN) 用于PD诊断.
- 囊网络被用于增强功能提取和表示.
- 应用了子域适应转移学习来调整本地数据分布并减轻子域混.
主要成果:
- 在GIS的现场数据上,SACN实现了93.75%的诊断准确度.
- 与传统的深度学习技术相比,拟议的SACN表现优越.
- 该方法有效地改善了特征表示,并适应了现场数据的变化.
结论:
- 亚域适应囊网络 (SACN) 为GIS现场应用中高精度PD诊断提供了可行和有效的解决方案.
- SACN提高特征表示和适应子域变化的能力突显了其在现实世界电力系统监控方面的潜力.
- 这种方法为通过先进的诊断能力提高气体绝缘开关设备的可靠性和安全性提供了显著的潜力.
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