Dynamic fault diagnosis means of the power message system based on big data

被引：0
作者：
He D. [1 ]
Chen T. [1 ]
Huang H. [1 ]
Qiu W. [1 ]
Tang Y. [1 ]
Jiang J. [1 ]
机构：
[1] State Grid ZheJiang Electric Power Corporation, Information and Telecommunication Branch, Zhejiang
来源：
International Journal of Information and Communication Technology | 2022年 / 20卷 / 01期
关键词：
Big data; Detection; Dynamic diagnosis; Electricity message system; Fault;
D O I：
10.1504/IJICT.2022.119320
中图分类号：
学科分类号：
摘要：
Aiming at the poor fault diagnosis ability of traditional power information system, a dynamic fault diagnosis method based on big data for power information system is proposed. Firstly, the original fault information of power information system is sampled, and the collected fault characteristic data are reconstructed by multi feature and information fitting. Then, the attribute distribution detection and big data mining are carried out for the fault dynamic characteristics of power information system. According to the high-order spectrum feature distribution of the extracted power information system fault signals, the dynamic fault diagnosis and fuzzy clustering analysis are carried out for the power information system, and the fault diagnosis is optimised according to the classification results. The simulation results show that the dynamic fault diagnosis accuracy of power information system is high, the fault sample detection results are accurate and reliable, and the dynamic fault detection ability is improved. Copyright © 2022 Inderscience Enterprises Ltd.
引用
收藏
页码：83 / 96
页数：13
相关论文
共 37 条
  • [1] Badrinarayanan V., Kendall A., Cipolla R., SegNet: a deep convolutional encoder-decoder architecture for image segmentation, IEEE Transactions on Pattern Analysis and Machine Intelligence, 39, 12, pp. 2481-2495, (2017)
  • [2] Bi K., An Q., Duan J., Et al., Fast diagnostic method of open circuit fault for modular multilevel DC/DC converter applied in energy storage system, IEEE Transactions on Power Electronics, 45, 5, (2017)
  • [3] Brahma S., Kavasseri R., Cao H., Et al., Real-time identification of dynamic events in power systems using PMU data, and potential applications-models, promises, and challenges, IEEE Transactions on Power Delivery, 26, 1, (2016)
  • [4] Gong Y., Su X., Qian H., Et al., Research on fault diagnosis methods for the reactor coolant system of nuclear power plant based on D-S evidence theory, Annals of Nuclear Energy, 112, 18, pp. 395-399, (2018)
  • [5] Guo S., Norris S., Bialek J., Adaptive parameter estimation of power system dynamic model using modal information, IEEE Transactions on Power Systems, 29, 6, pp. 2854-2861, (2014)
  • [6] Han Y.C., Tong X.Y., Power grid fault diagnosis method based on dynamic reasoning chain, Power Grid Technology, 41, pp. 1315-1323, (2017)
  • [7] Hang J., Wu H., Ding S., Et al., A DC-flux-injection method for fault diagnosis of high-resistance connection in direct-torque-controlled PMSM drive system, IEEE Transactions on Power Electronics, 57, 99, (2019)
  • [8] Hu Z., Li X., A new method considering the dynamic coupling characteristic in power system for stability assessment, IET Generation Transmission & Distribution, 11, 10, pp. 2534-2539, (2017)
  • [9] Jiang B., Guo Z., Zhu Q., Et al., Dynamic minimax probability machine-based approach for fault diagnosis using pairwise discriminate analysis, IEEE Transactions on Control Systems Technology, 27, 2, pp. 806-813, (2019)
  • [10] Ju B., Qian Y.T., Ye M.C., Collaborative filtering algorithm based on structured projective nonnegative matrix factorization, Journal of Zhejiang University: Engineering Science, 49, 7, pp. 1319-1325, (2015)
1234