The DOI matches its Crossref record; 28 of the 28 references it cites were found in a registry or an index.
Cascaded H-Bridge Multilevel Inverter System Fault Diagnosis Using a PCA and Multiclass Relevance Vector Machine Approach
Abstract
Multilevel inverters, for their distinctive performance, have been widely used in high voltage and high-power applications in recent years. As power electronics equipment reliability is very important and to ensure multilevel inverter systems stable operation, it is important to detect and locate faults as quickly as possible. In this context and to improve fault diagnosis accuracy and efficiency of a cascaded H-bridge multilevel inverter system (CHMLIS), a fault diagnosis strategy based on the principle component analysis and the multiclass relevance vector machine (PCA-mRVM), is elaborated and proposed in this paper. First, CHMLIS output voltage signals are selected as input fault classification characteristic signals. Then, a fast Fourier transform is used to preprocess these signals. PCA is used to extract fault signals features and to reduce samples dimensions. Finally, an mRVM model is used to classify faulty samples. Compared to traditional approaches, the proposed PCA-mRVM strategy not only achieves higher model sparsity and shorter diagnosis time, but also provides probabilistic outputs for every class membership. Experimental tests are carried out to highlight the proposed PCA-mRVM diagnosis performances.
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Cites
Bayesian Analysis of Binary and Polychotomous Response Data
Albert, J. H., & Chib, S. (1993) · Journal of the American Statistical Association · doi:10.1080/01621459.1993.10476321 · Cited by 3,167
Transitioning to Physics-of-Failure as a Reliability Driver in Power Electronics
Wang, H., Liserre, M., Blaabjerg, F., de Place Rimmen, P., Jacobsen, J. B., Kvisgaard, T., & Landkildehus, J. (2014) · IEEE Journal of Emerging and Selected Topics in Power Electronics · doi:10.1109/jestpe.2013.2290282 · Cited by 693
A Generalized Cascaded Multilevel Inverter Using Series Connection of Submultilevel Inverters
Kangarlu, M. F., & Babaei, E. (2013) · IEEE Transactions on Power Electronics · doi:10.1109/tpel.2012.2203339 · Cited by 442
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
Islam, M. R., Guo, Y., & Zhu, J. (2014) · IEEE Transactions on Power Electronics · doi:10.1109/tpel.2013.2290313 · Cited by 301
Fault Diagnostic System for a Multilevel Inverter Using a Neural Network
Khomfoi, S., & Tolbert, L. M. (2007) · IEEE Transactions on Power Electronics · doi:10.1109/tpel.2007.897128 · Cited by 291
Fault-Tolerant Voltage Source Inverter for Permanent Magnet Drives
Errabelli, R. R., & Mutschler, P. (2012) · IEEE Transactions on Power Electronics · doi:10.1109/tpel.2011.2135866 · Cited by 278
Liu, J., Cheng, K. W. E., & Ye, Y. (2014) · IEEE Transactions on Power Electronics · doi:10.1109/tpel.2013.2291514 · Cited by 277
Open-Circuit Fault Diagnosis in PMSG Drives for Wind Turbine Applications
Freire, N. M. A., Estima, J. O., & Marques Cardoso, A. J. (2013) · IEEE Transactions on Industrial Electronics · doi:10.1109/tie.2012.2207655 · Cited by 277
Jung, S.-M., Park, J.-S., Kim, H.-W., Cho, K.-Y., & Youn, M.-J. (2013) · IEEE Transactions on Power Electronics · doi:10.1109/tpel.2012.2212916 · Cited by 271
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An Overview of Artificial Intelligence Applications for Power Electronics
Zhao, S., Blaabjerg, F., & Wang, H. (2021) · IEEE Transactions on Power Electronics · doi:10.1109/tpel.2020.3024914 · Cited by 947
A review of current issues of marine current turbine blade fault detection
Xie, T., Wang, T., He, Q., Diallo, D., & Claramunt, C. (2020) · Ocean Engineering · doi:10.1016/j.oceaneng.2020.108194 · Cited by 72
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Metadata
- Authors
- Wang, T., Xu, H., Han, J., Elbouchikhi, E., & Benbouzid, M. E. H.
- Venue
- IEEE Transactions on Power Electronics
- Published
- 2015-12
- Volume
- 30
- Issue
- 12
- Pages
- 7006-7018
- ISSN
- 0885-8993 (ISSN-L)
- 1941-0107
- OpenAlex
- W2051044060
- Cited by
- 273
- References
- 28
- FWCI
- 9.78
- Type
- journal article
- Language
- en
- Open access
- closed
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- Wang, T., Xu, H., Han, J., Elbouchikhi, E., & Benbouzid, M. E. H. (2015). Cascaded H-Bridge Multilevel Inverter System Fault Diagnosis Using a PCA and Multiclass Relevance Vector Machine Approach. IEEE Transactions on Power Electronics. https://doi.org/10.1109/tpel.2015.2393373
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