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Prediction of Dissolved Gas Concentration in Oil Based on Fuzzy Time Series

  • Jun Liu
  • Lijin Zhao
  • Liang Huang
  • Huarong Zeng
  • Xun Zhang
  • Hui Peng
Conference paper
Part of the Advances in Intelligent Systems and Computing book series (AISC, volume 754)

Abstract

The prediction of dissolved gas content in transformer oil is helpful for early detection of latent faults in transformer, and it has important guiding significance for better condition based maintenance. In view of the abundant data of transformer DGA, and that the trend of the change of dissolved gas content in oil under normal running condition is not obvious, a prediction method based on fuzzy time series model is proposed. Consider that the change in dissolved gas content in oil is interaction and influenced, in this paper, the classical fuzzy time series model is improved from the view of domain division, and propose a multi factor fuzzy time series model based on spatial FCM domain partition. The example analysis shows that the method can well fit the changing trend of DGA data, and compared with the classic fuzzy time series model and the one-dimensional FCM fuzzy time series model, the superiority of the improved model in prediction is verified.

Keyword

Power transformer Dissolved gas analysis Fuzzy time series Data prediction 

References

  1. 1.
    Fu, B.: Research on Transformer Fault Diagnosis and Prediction Based on Particle Swarm Optimization. Hua Qiao University (2012)Google Scholar
  2. 2.
    Zhang, Y.: Transformer Fault Diagnosis and Prediction Based on Particle Swarm Optimization Support Vector Machine. Xihua University (2011)Google Scholar
  3. 3.
    Azizzadeh, L., Zadeh, L., et al.: Information and Control. Fuzzy Sets 8(3), 338–353 (1965)Google Scholar
  4. 4.
    Li, G.: Intelligent Predictive Control and Its Realization of MATLAB. Publishing House of Electronics Industry Beijing (2010)Google Scholar
  5. 5.
    Song, Q., Chissom, B.S.: Forecasting enrollments with fuzzy time series—part I. Fuzzy Sets Syst. 62(1), 1–8 (1994)CrossRefGoogle Scholar
  6. 6.
    Chen, S.M.: Forecasting enrollments based on fuzzy time series. Fuzzy Sets Syst. 81(3), 311–319 (1996)CrossRefGoogle Scholar
  7. 7.
    Qiu W.: Fuzzy time series model and its application in stock index trend analysis. Dalian University of Technology (2012)Google Scholar
  8. 8.
    Song, Q., Chissom, B.S.: Fuzzy time series and its model. Fuzzy Sets Syst. 54(3), 269–277 (1993)MathSciNetCrossRefGoogle Scholar
  9. 9.
    Chen, S.M.: Forecasting enrollments based on fuzzy time series. Fuzzy Sets Syst. 81(3), 311–319 (1996)CrossRefGoogle Scholar
  10. 10.
    Hwang, J.R., Chen, S.M., Lee, C.H.: Handling forecasting problems using fuzzy time series. Fuzzy Sets Syst. 100(1–3), 217–228 (1998)CrossRefGoogle Scholar
  11. 11.
    Lee, M.H., Efendi, R., Ismail, Z.: Modified weighted for enrollment forecasting based on fuzzy time series. J. Artif. Intell. 25(1) (2009) Google Scholar
  12. 12.
    Wang, J., Wang, S., Bao, F.: A fast fuzzy C mean clustering algorithm based on spatial distance. Comput. Eng. Appl. 51(1), 177–183 (2015)Google Scholar
  13. 13.
    Ma, Y.: The study of multi factor fuzzy time series forecasting model. Dalian Maritime University (2016)Google Scholar
  14. 14.
    Zhenyong, Yang: Discussion on judgement of transformer fault by using guidelines for the analysis and judgement of dissolved gases in transformer oil. Transformer 45(10), 24–27 (2008)Google Scholar
  15. 15.
    Donghua, Zhou: Data processing of gas well production based on fusion of smoothing algorithm and wavelet transform. Oil Gas Field Surf. Eng. 30(4), 33–35 (2011)Google Scholar
  16. 16.
    Chen, X.: Research on Transformer Fault Prediction Method Based on Limit Learning Machine. North China Electric Power University (2015)Google Scholar
  17. 17.
    Wang, T., Xia, T., Cao, X.: Feature dimension reduction algorithm based prediction method for protein quaternary structure. Int. J. Wirel. Microwave Technol. (IJWMT) 2(5), 28–33 (2012)CrossRefGoogle Scholar
  18. 18.
    Man, D.-P., Li, X.-Z., Yang, W., Wang, W., Xuan, S.-C.: A Multi-step attack recognition and prediction method via mining attacks conversion frequencies. Int. J. Wirel. Microw. Technol. (IJWMT) 2(2), 20–25 (2012).  https://doi.org/10.5815/ijwmt.2012.02.04CrossRefGoogle Scholar
  19. 19.
    Sarailoo, M., Rahmani, Z., Rezaie, B.: Fuzzy predictive control of step-down DC-DC converter based on hybrid system approach. Int. J. Intell. Syst. Appl. (IJISA) 6(2), 1–13 (2014).  https://doi.org/10.5815/ijisa.2014.02.01CrossRefGoogle Scholar
  20. 20.
    Zhao, Y., Jin, H., Wang, L., Wang, S.: Rough neuron network for fault diagnosis. Int. J. Image Graph. Signal Process. (IJIGSP) 3(2), 51–58 (2011).  https://doi.org/10.5815/ijigsp.2011.02.08CrossRefGoogle Scholar

Copyright information

© Springer International Publishing AG, part of Springer Nature 2019

Authors and Affiliations

  • Jun Liu
    • 1
  • Lijin Zhao
    • 1
  • Liang Huang
    • 1
  • Huarong Zeng
    • 1
  • Xun Zhang
    • 1
  • Hui Peng
    • 2
  1. 1.Electric Power Research Institution of Guizhou Power GridGuiyangChina
  2. 2.School of Electrical EngineeringWuhan UniversityWuhanChina

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