由IHBA优化的DR-SE-NPCNet用于在混合和噪音条件下在三级NPC逆变器中进行可靠的开放电路故障诊断
Qisheng Liu1, Changxi Chen2, Honglin Ouyang2
1College of Electrical and Information Engineering, Hunan University, Changsha, 410082, China. liuqisheng@hnu.edu.cn.
Scientific reports
|December 27, 2025
概括
本研究引入了一种新方法,用于检测三级中性点紧 (NPC) 逆变器的开放电路故障 (OCF). 综合框架提高了诊断的准确性和可靠性,即使在噪音条件下.
科学领域:
- 电气工程 电气工程
- 电力电子 电力电子 电力电子
- 人工智能的人工智能
背景情况:
- 三级中性点紧 (NPC) 逆变器的开放电路故障 (OCF) 会降低功率质量和系统可靠性.
- 现有的诊断方法在混合操作条件,高噪音水平和超参数灵敏度等方面扎.
研究的目的:
- 提出一个综合模型优化框架,用于在NPC逆变器中强大的OCF诊断.
- 在具有挑战性的环境和操作条件下提高诊断准确性和可靠性.
主要方法:
- 开发了DR-SE-NPCNet,这是一个轻量级的诊断网络,具有时间分辨率维护-时间扩张 (TRP-TD) 骨干和残余和挤压刺激校准融合 (ReSE-CF) 模块.
- 采用了改进的蜂蜜子算法 (IHBA) 进行高效和强大的全球超参数优化.
- 在硬件NPC逆变器平台上验证了框架.
主要成果:
- 在10 dB的噪音和混合操作条件下 (负载,调制指数,直流总线电压,输出频率) 达到92.83-96.94%的准确性.
- 性能优于传统的基于卷积神经网络 (CNN) 的方法.
- 通过IHBA优化,诊断准确度进一步提高了2-3%.
结论:
- 集成的DR-SE-NPCNet和IHBA框架为OCF诊断提供了一个强大的,高精度的解决方案.
- 拟议的方法有效地解决了现有诊断技术在杂和可变环境中的局限性.
相关概念视频
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