The DOI matches its Crossref record; 51 of the 51 references it cites were found in a registry or an index.
Model-Based Fault Detection and Identification for Switching Power Converters
Abstract
We present the analysis, design, and experimental validation of a model-based fault detection and identification (FDI) method for switching power converters using a model-based state estimator approach. The proposed FDI approach is general in that it can be used to detect and identify arbitrary faults in components and sensors in a broad class of switching power converters. The FDI approach is experimentally demonstrated on a nanogrid prototype with a 380-V dc distribution bus. The nanogrid consists of four different switching power converters, including a buck converter, an interleaved boost converter, a single-phase rectifier, and a three-phase inverter. We construct a library of fault signatures for possible component and sensor faults in all four converters. The FDI algorithm successfully achieves fault detection in under 400 $\mu$s and fault identification in under 10 ms for faults in each converter. The proposed FDI approach enables a flexible and scalable solution for improving fault tolerance and awareness in power electronics systems.
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Cites
Principles of Power Electronics
Kassakian, J. G., Perreault, D. J., Verghese, G. C., & Schlecht, M. F. (2023) · Cambridge University Press eBooks · doi:10.1017/9781009023894 · Cited by 1,394
Issues of Fault Diagnosis for Dynamic Systems
(2000) · Springer eBooks · doi:10.1007/978-1-4471-3644-6 · Cited by 1,330
Dependability: Basic Concepts and Terminology
Laprie, J. C. (1992) · Dependable Computing and Fault-Tolerant Systems · doi:10.1007/978-3-7091-9170-5_1 · Cited by 745
Zero Energy Buildings: A Critical Look at the Definition
Torcellini, P., Pless, S., Deru, M. P., & Crawley, D. B. (2006) · Cited by 608
Survey on Fault-Tolerant Techniques for Power Electronic Converters
Zhang, W., Xu, D., Enjeti, P. N., Li, H., Hawke, J. T., & Krishnamoorthy, H. S. (2014) · IEEE Transactions on Power Electronics · doi:10.1109/tpel.2014.2304561 · Cited by 587
Fault detection of open-switch damage in voltage-fed PWM motor drive systems
de Araujo Ribeiro, R. L., Jacobina, C. B., da Silva, E. R. C., & Lima, A. M. N. (2003) · IEEE Transactions on Power Electronics · doi:10.1109/tpel.2003.809351 · Cited by 441
Fault detection and isolation on a PWM inverter by knowledge-based model
Peuget, R., Courtine, S., & Rognon, J.-P. (1998) · IEEE Transactions on Industry Applications · doi:10.1109/28.739017 · Cited by 384
Estima, J. O., & Marques Cardoso, A. J. (2013) · IEEE Transactions on Industrial Electronics · doi:10.1109/tie.2012.2188877 · Cited by 377
Fault Detection for Modular Multilevel Converters Based on Sliding Mode Observer
Shao, S., Wheeler, P. W., Clare, J. C., & Watson, A. J. (2013) · IEEE Transactions on Power Electronics · doi:10.1109/tpel.2013.2242093 · Cited by 361
National Renewable Energy Laboratory
(2010) · Choice Reviews Online · doi:10.5860/choice.47-5675 · Cited by 350
Cited by
A Digital Twin Approach for Fault Diagnosis in Distributed Photovoltaic Systems
Jain, P., Poon, J., Singh, J. P., Spanos, C., Sanders, S. R., & Panda, S. K. (2020) · IEEE Transactions on Power Electronics · doi:10.1109/tpel.2019.2911594 · Cited by 462
Self-citationAli, Z., Terriche, Y., Hoang, L. Q. N., Abbas, S. Z., Hassan, M. A., Sadiq, M., Su, C.-L., & Guerrero, J. M. (2021) · IEEE Access · doi:10.1109/access.2021.3112383 · Cited by 104
Metadata
- Authors
- Poon, J., Jain, P., Konstantakopoulos, I. C., Spanos, C., Panda, S. K., & Sanders, S. R.
- Venue
- IEEE Transactions on Power Electronics
- Published
- 2017-02
- Volume
- 32
- Issue
- 2
- Pages
- 1419-1430
- ISSN
- 0885-8993 (ISSN-L)
- 1941-0107
- OpenAlex
- W2318970278
- Cited by
- 257
- References
- 51
- FWCI
- 18.80
- Type
- journal article
- Language
- en
- Open access
- closed
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