论文标题
优化数字双胞胎用于网格连接的逆变器中的故障诊断 - 一种贝叶斯方法
Optimizing a Digital Twin for Fault Diagnosis in Grid Connected Inverters -- A Bayesian Approach
论文作者
论文摘要
在本文中，基于高参数调音的数字双胞胎的贝叶斯优化用于诊断网格连接的逆变器中的各种故障。由于故障检测和诊断需要很高的精度，因此我们将精力用于在线优化数字双胞胎，这反过来又可以灵活地实现，具有有限的数据。结果，所提出的框架不仅成为具有有限数据的数字双胞胎设计模型版本和部署的实用解决方案，而且还允许集成深度学习工具以提高参数调谐功能。对于分类性能评估，我们考虑了虚拟同步发电机（VSG）控制的网格形成转换器中的不同故障案例，并证明了我们方法的疗效。我们的研究成果揭示了我们的数字双胞胎设计所取得的准确性和忠诚度的提高，从而克服了传统的超参数调谐方法的缺点。
In this paper, a hyperparameter tuning based Bayesian optimization of digital twins is carried out to diagnose various faults in grid connected inverters. As fault detection and diagnosis require very high precision, we channelize our efforts towards an online optimization of the digital twins, which, in turn, allows a flexible implementation with limited amount of data. As a result, the proposed framework not only becomes a practical solution for model versioning and deployment of digital twins design with limited data, but also allows integration of deep learning tools to improve the hyperparameter tuning capabilities. For classification performance assessment, we consider different fault cases in virtual synchronous generator (VSG) controlled grid-forming converters and demonstrate the efficacy of our approach. Our research outcomes reveal the increased accuracy and fidelity levels achieved by our digital twin design, overcoming the shortcomings of traditional hyperparameter tuning methods.
