Peter, Naithan and Kumar, Rahul R. and Chand, Shyamal S. and Cirrincione, Maurizio (2024) Enhancing Open Circuit Switch Fault Localization in Two-Level Voltage Source Inverters through Machine Learning with 3D Current Trajectory Analysis. [Conference Proceedings]
Full text not available from this repository.Abstract
This paper proposes an enhanced approach for localizing open circuit faults (OCFs) in two-level voltage source inverters (VSIs). OCFs in VSIs can lead to critical system failures which can also be catastrophic if not localized or attended to within a small time-frame. While detecting the OCFs is important, accurate localization of OCFs is paramount as it helps in reducing the downtime of the system. Leveraging machine learning algorithms, including a neural-based classifier called the Wide Neural Network (NN) and a non-neural classifier known as the Subspace KNN, the proposed study demonstrates promising results. The Wide NN achieves an accuracy of 97.16%, while the Subspace KNN achieves an impressive accuracy of 99.66% in fault localization. These high accuracies contribute to improved VSI reliability in various industrial applications, advancing the field of power electronics by exploring innovative fault detection and localization techniques.
| Item Type: | Conference Proceedings |
|---|---|
| Uncontrolled Keywords: | Location awareness;Uncertainty;Voltage source inverters;Fault detection;Artificial neural networks;Electrical fault detection;Integrated circuit reliability;Open Circuit Fault;3D Current Trajectory;Machine Learning;Voltage Source Inverter |
| Subjects: | T Technology > TK Electrical engineering. Electronics Nuclear engineering |
| Divisions: | School of Information Technology, Engineering, Mathematics and Physics (STEMP) |
| Depositing User: | Rahul Kumar |
| Date Deposited: | 07 Mar 2024 23:12 |
| Last Modified: | 14 Mar 2024 23:03 |
| URI: | https://repository.usp.ac.fj/id/eprint/14463 |
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