Kumar, Rahul R. and Kumar, Shanal S. and Cirrincione, Giansalvo and Cirrincione, Maurizio and Guilbert, Damien and Ram, Krishnil R. and Mohammadi, Ali (2021) Power Switch Open - Circuit Fault-Diagnosis Based on a Shallow Long - Short Term Memory Neural Network: Investigation of an Interleaved Buck Converter for Electrolyzer applications. [Conference Proceedings]
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Abstract
Hydrogen energy conversion using Fuel Cells is very promising for standalone power as well as transportation applications. Hydrogen gas production using renewable energy sources is possible through the use of electrolyzers in which DC-DC converters play an important role. This paper presents an accurate and robust method of fault diagnosis and condition monitoring applied to an interleaved DC/DC buck converter that supplies a proton exchange membrane (PEM) electrolyzer. This work mainly focuses on power switch open-circuit failures. The study gives excellent results in the early detection of faults to improve the reliability of PEM electrolyzers. A suitable experimental test bench has been realized to obtain data under healthy and faulty operating conditions. A preliminary exploratory analysis of the data has been carried out using a linear approach to understand the geometry of the data and suggest a suitable tool for classifying the system’s condition. The paper then proposes a shallow configuration Long Short Term Memory (LSTM) based neural network capable of detecting and localizing the fault at every time step without any preprocessing. The experimental results presented in this paper show that, after a detailed comparison with other 25 neural and non-neural based techniques for classification, the shallow LSTM neural network gives the best results.
| Item Type: | Conference Proceedings |
|---|---|
| Subjects: | Q Science > Q Science (General) > Q1-295 General T Technology > TK Electrical engineering. Electronics Nuclear engineering T Technology > TP Chemical technology |
| Divisions: | School of Information Technology, Engineering, Mathematics and Physics (STEMP) |
| Depositing User: | Rahul Kumar |
| Date Deposited: | 05 Feb 2024 00:27 |
| Last Modified: | 05 Feb 2024 00:27 |
| URI: | https://repository.usp.ac.fj/id/eprint/14021 |
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