电力电子逆变器开路故障智能诊断的关键要素优化方法
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1.海军工程大学 电磁能技术全国重点实验室;2.湖北东湖实验室

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TM93

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国家自然科学基金青年项目(52407230);国防科技重点实验室基金项目(614221725010201)


Optimization Methods for Key Elements in Intelligent Diagnosis of Open-Circuit Faults in Power Electronic Inverters
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    摘要:

    为了解决电力电子逆变器开路故障智能诊断面临的实际故障样本缺乏、变特征适应性问题,相应从数据、算法两大智能化要素角度提出了一套优化方法,以支撑电力电子逆变器开路故障智能诊断的实际应用。数据要素方面,提出基于逆变特性的故障样本扩增方法,明确了诊断模型训练所需的最少实际样本量;算法要素方面,提出一种诊断模型注意力增强方法,以及模型频率点自适应训练方法,显著提高了面对逆变器宽频运行的模型训练效果及诊断准确率。实验验证了上述智能化要素优化方法的有效性。

    Abstract:

    To address the challenges of intelligent diagnosis for open-circuit faults in power electronic inverters, such as the lack of actual fault samples and the issue of varying characteristic adaptability, a set of optimization methods was proposed from two key intelligent elements: data and algorithm, to support the practical applications of intelligent diagnosis for open-circuit faults in power electronic inverters. For the data element, a fault sample amplification method based on inverters′ characteristics was proposed, which finds out the minimum number of practical samples required for model training. For the algorithm element, an attention-enhanced method and a frequency points adaptive training method for the diagnosis model were proposed, which significantly improve model training effectiveness and diagnosis accuracy under wide-frequency inverter operation. The effectiveness of the proposed optimization methods for the intelligent elements was validated by experiments.

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历史
  • 收稿日期:2025-06-27
  • 最后修改日期:2025-09-18
  • 录用日期:2025-09-18
  • 在线发布日期: 2025-09-29
  • 出版日期:
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