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.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
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).
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2017 Springer Nature Singapore Pte Ltd.
About this paper
Cite this paper
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
Download citation
DOI: https://doi.org/10.1007/978-981-10-6364-0_18
Published:
Publisher Name: Springer, Singapore
Print ISBN: 978-981-10-6363-3
Online ISBN: 978-981-10-6364-0
eBook Packages: Computer ScienceComputer Science (R0)