Abstract
Nowadays, great progress has been made in the development of multilevel inverters in renewable energy sources and other electrical drive applications. A k-Nearest Neighbors (k-NN) algorithm is applied to fault diagnosis of Cascaded H-Bridge Multilevel Inverter (CHMLI), this new fault diagnosis method is based on Probabilistic Principle Component Analysis (PPCA). The output voltage signals under different fault conditions of CHMLI are taken as the fault characteristics signals to avoid the effect of load variation on fault diagnosis. PPCA is used to optimize the data without changing the original properties of the input data, and k-NN is used to identify the accurate fault location and diagnosis the fault. The proposed technique is validated by conducting the experiment using Field-Programmable Gate Array (FPGA) controller. The simulation and experimental results shows that the proposed fault diagnosis method reduced the fault diagnosis time and improved the accuracy.
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References
Mariethoz, S.: Systematic design of high-performance hybrid cascaded multilevel inverters with active voltage balance and minimum switching losses. IEEE Trans. Power Electron. 28, 3100–3113 (2013)
Khomfoi, S., Tolbert, L.M.: Fault diagnosis and reconfiguration for multilevel inverter drive using AI-based techniques. IEEE Trans. Ind. Electron. 54, 2954–2968 (2007)
Khomfoi, S., Tolbert, L.M.: Fault diagnostic system for a multilevel inverter using a neural network. IEEE Trans. Power Electron. 22, 1062–1069 (2007)
Wang, T., Xu, H., Han, J., Elbouchikhi, E., Benbouzid, M.E.H.: Cascaded H-bridge multilevel inverter system fault diagnosis using a PCA and multiclass relevance vector machine approach. IEEE Trans. Power Electron. 30, 7006–7018 (2015)
Lu, B., Sharma, S.K.: A literature review of IGBT fault diagnostic and protection methods for power inverters. IEEE Trans. Ind. Appl. 45(5), 1770–1777 (2009)
Keswani, R.A., Suryawanshi, H.M., Ballal, M.S.: Multi-resolution analysis for converter switch faults identification. IET Power Electron. 8(5), 783–792 (2015)
Keswani, R.A., Suryawanshi, H.M., Ballal, M.S., Renge, M.M.: Wavelet modulus maxima for single switch open fault in multi-level inverter. Electric Power Compon. Syst. 42(9), 889–900 (2014)
Lezana, P., Pou, J., Meynard, T.A., Rodriguez, J., Ceballos, S., Richardeau, F.: Survey on fault operation on multilevel inverters. IEEE Trans. Ind. Electron. 57(7), 2207–2218 (2010)
Palanivel, P., Dash, S.S.: Analysis of THD and output voltage performance for cascaded multilevel inverter using carrier pulse width modulation techniques. IET Power Electron. 4, 951–958 (2011)
Tipping, M.E., Bishop, C.M.: Mixtures of Probabilistic Principle Component Analysers, pp. 443–482. MIT Press, Cambridge (2006)
Tipping, M.E., Bishop, C.M.: Probabilistic principal component analysis. J. R. Stat. Soc. 61(3), 611–622 (1999)
Benameur, S., Mignotte, M., Destrempes, F., Guise, J.A.D.: Three-dimensional biplanar reconstruction of scoliotic rib cage using the estimation of a mixture of probabilistic prior models. IEEE Trans. Biomed. Eng. 52, 1713–1728 (2005)
Jon, E., Kim, D.K., Kim, N.S.: Robust correlation estimation for EMAP-based speaker adaptation. IEEE Sig. Process. Lett. 8, 184–186 (2001)
Kim, D.K., Kim, N.S.: Rapid speaker adaptation using probabilistic principal component analysis. IEEE Sig. Process. Lett. 8, 180–183 (2001)
He, Q.P., Wang, J.: Fault detection using the k-Nearest neighbor rule for semiconductor manufacturing processes. IEEE Trans. Semicond. Manuf. 20(4), 345–354 (2007)
Zhou, Z., Wen, C., Yang, C.: Fault detection using random projections and k-Nearest neighbor rule for semiconductor manufacturing processes. IEEE Trans. Semicond. Manuf. 28(1), 70–79 (2015)
Acknowledgements
This work was supported by the National Natural Science Foundation of China under Grant No. 51577046, The State Key Program of National Natural Science Foundation of China under Grant No. 51637004, The national key research and development plan “Important Scientific Instruments and Equipment Development” of China Under Grant No. 2016YFF0102200.
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Kuraku, N.V.P., Ali, M., He, Y. (2018). Open Circuit Fault Diagnosis of Cascaded H-Bridge MLI Using k-NN Classifier Based on PPCA. In: Hassanien, A., Tolba, M., Elhoseny, M., Mostafa, M. (eds) The International Conference on Advanced Machine Learning Technologies and Applications (AMLTA2018). AMLTA 2018. Advances in Intelligent Systems and Computing, vol 723. Springer, Cham. https://doi.org/10.1007/978-3-319-74690-6_42
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DOI: https://doi.org/10.1007/978-3-319-74690-6_42
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