Abstract
Accurate fault diagnosis is the premise to ensure the safe and reliable operation of photovoltaic three-level inverter. A fault diagnosis method based on wavelet neural network is researched in the paper. First of all, the topology and the fault characteristics of three-level inverter are analyzed, the fault features are analyzed for three-level inverter when single and double IGBTs fault, the eigenvectors of phase voltage, the upper bridge arm and the lower bridge arm voltage are extracted by three-layer Wavelet Package Transform, the BP neural network is designed for training data and testing. The simulation model is built by Matlab/Simulink, the simulation results show that the method can accurately diagnose for various fault circumstances.
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References
Ding, M., Wang, W., Wang, X., et al.: A review on the effect of large-scale PV generation on power systems. Proc. CSEE 34(1), 1–14 (2014)
Li, N., Wang, Y., Lei, W., et al.: Research on equivalent relations between two kinds of SVPWM strategies and SPWM strategy for three-level neutral point clamped inverter. Power Syst. Technol. 38(5), 1283–1290 (2014)
Bendre, A., Cuzner, R., Krstic, S.: Three-level inverter system. IEEE Ind. Appl. Mag. 15(2), 12–23 (2009)
Quntao, A., Li, S., Lizhi, S., et al.: Recent developments of fault diagnosis methods for switches in three-phase inverters. Trans. China Electrotech. Soc. 26(4), 135–144 (2011)
Chen, D., Ye, Y., Hua, R.: Fault diagnosis for three-level inverter of CRH based on real-time waveform analysis. Trans. China Electrotech. Soc. 29(6), 106–113 (2014)
Wan, X., Hu, H., Yu, Y., et al.: Survey of fault detection and diagnosis technology for three-level inverter of photovoltaic. J. Electr. Meas. Instrum. 29(12), 1727–1738 (2015)
Shang, W., He, Z., Hu, H., et al.: An IGBT output power-based diagnosis of open-circuit fault in inverter. Power Syst. Technol. 37(4), 1140–1145 (2013)
Junbo, L., Mahemuti, P., Chan, Z., et al.: Study on open-circuit fault diagnosis of the IGBT in three-level inverter. Electr. Meas. Instrum. 52(20), 35–40 (2015)
Chen, C., Chen, D., Ye, Y.: The neural network-based diagnostic method for atypical faults in NPC three-level inverter. In: Chinese Control and Decision Conference (CCDC), pp. 4740–4745(2013)
Fan, J., Yi, Y.: Fault diagnosis of photovoltaic grid-connected inverter based on wavelet analysis. High Power Invert. Technol. 5, 12–16 (2014)
Chen, D., Ye, Y., Hua, R.: Fault diagnosis of three-level inverter based on wavelet analysis and bayesian classifier. In: Chinese Control and Decision Conference (CCDC), pp. 4777–4779 (2013)
Jiang, Y., Wang, Y., Youren, T., et al.: Online multiple fault diagnosis for PV inverter based on wavelet packet energy spectrum and extreme learning machine. Chin. J. Sci. Instrum. 36(9), 2145–2152 (2015)
Guoyong, L.: Neural Fuzzy Predictive Control MATLAB Implementation. Publishing House of Electronics Industry, Beijing (2013)
Acknowledgments
The work described in this paper is fully supported by a grant from the National Natural Science Foundation (No. 71263043).
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Yang, G., Wang, P., Li, B., Lei, B., Tang, H., Li, R. (2017). Fault Diagnosis Method of Ningxia Photovoltaic Inverter Based on Wavelet Neural Network. In: Li, K., Xue, Y., Cui, S., Niu, Q., Yang, Z., Luk, P. (eds) Advanced Computational Methods in Energy, Power, Electric Vehicles, and Their Integration. ICSEE LSMS 2017 2017. Communications in Computer and Information Science, vol 763. Springer, Singapore. https://doi.org/10.1007/978-981-10-6364-0_18
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DOI: https://doi.org/10.1007/978-981-10-6364-0_18
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