Abstract
To address the subjective experience in medium- and low-voltage (MV/LV) switch fault diagnosis and the deviation between diagnosis results and the actual occurrence, this paper provides an improved dynamic adaptive fuzzy Petri net-based method for diagnosing MV/LV switch faults. The effectiveness of this model is then verified by combining typical MV/LV switch fault cases. The research results show that our proposed method can effectively deal with the uncertainty factors in the fault probability and has an excellent performance in fault tolerance and high operational efficiency.
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References
Awadallah, M.A., Morcos, M.M.: Automatic diagnosis and location of open-switch fault in brushless DC motor drives using wavelets and neuro-fuzzy systems. IEEE Trans. Energy Convers. 21(1), 104–111 (2006)
Cai, G.B., Chang-Hua, H.U., Cai, Y.N., et al.: Diagnosis of switch/relay circuit fault based on qualitative reasoning. J. Syst. Simul. 32(2), 12–16 (2006)
Mohammed, O.D., Rantatalo, M., Aidanpaeae, J.O., et al.: Vibration signal analysis for gear fault diagnosis with various crack progression scenarios. Mech. Syst. Signal Process. 41(1–2), 176–195 (2013)
Guo, Y., Chen, S., Shaohua, L.I., et al.: Mechanical fault diagnosis method of high-voltage disconnector based on empirical modal decomposition and support vector machine. High Voltage Apparatus 54(9), 12–18 (2018)
Huang, R., Chen, Y.W., Chen, L.C., et al.: Mechanical states diagnosis system the panel switch based on the analysis of vibration signal. Electric Switchgear 53(2), 21–26 (2015)
Jing, S., Qin, S.Y., Song, Y.H.: Fault diagnosis of electric power systems based on fuzzy petri nets. IEEE Trans. Power Syst. 19(4), 2053–2059 (2004)
Liu, H.C., Lin, Q.L., Ren, M.L.: Fault diagnosis and cause analysis using fuzzy evidential reasoning approach and dynamic adaptive fuzzy petri nets. Comput. Ind. Eng. 66(4), 899–908 (2013)
Xie, M., Wu, Y., Yan, Y., et al.: Power system fault diagnosis based on improved dynamic adaptive fuzzy petri nets and back propagation algorithm. Proceedings of the CSEE 35(12), 3008–3017 (2016)
Liu, H., Liu, L., Lin, Q., et al.: Knowledge acquisition and representation using fuzzy evidential reasoning and dynamic adaptive fuzzy petri nets. IEEE Trans. Syst. Man Cybern. 43(3), 1059–1072 (2013)
Lei, Y., Zhang, N., Li, Q., et al.: Fault diagnosis model of transformer equipment based on improved rough set theory and Bayesian network. Electronic Design Engineering 29(4), 126–130 (2021)
Acknowledgements
This work was supported by key science and technology project of China Southern Power Grid Corporation (Research and application of key technology of intelligent detection of Medium and low voltage switch, GZHKJXM20200030).
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Zhang, M., Fang, J., Wang, H., Wang, Y., He, J., Lin, X. (2023). A Fault Diagnosis Method for Medium- and Low-Voltage Switches Based on Improved Dynamic Adaptive Fuzzy Petri Net. In: Cao, W., Hu, C., Chen, X. (eds) Proceedings of the 3rd International Symposium on New Energy and Electrical Technology. ISNEET 2022. Lecture Notes in Electrical Engineering, vol 1017. Springer, Singapore. https://doi.org/10.1007/978-981-99-0553-9_85
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DOI: https://doi.org/10.1007/978-981-99-0553-9_85
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