Cluster Computing

, Volume 22, Supplement 2, pp 4527–4533 | Cite as

Multivariable bilinear subspace recursive likelihood identification for pumped storage motor

  • Zhuang XuEmail author
  • Ge Bao-Jun
  • Tao Dajun


To improve precision for model identification result of pumped storage motor, a kind of multivariable bilinear subspace recursive likelihood identification for pumped storage motor is put forward. Firstly, pumped storage motor magnetic filed equation is given, pumped storage motor model based on extension Kalman Filter is established, two-set identification models are constructed by coordination by utilizing two-group state vectors of resistance and inductance and two-set models work cooperatively to construct circulation identification algorithm; then, introduce subspace identification strategy, establish relevant data estimation equation by utilizing block Hankel matrix and realize valid identification to system parameter matrix by adopting least squares (LS); validity of proposed algorithm is verified by simulation experiment in matlab platform and hardware environment.


Pumped storage motor Multivariable Bilinear Subspace Recursive likelihood identification 



National Natural Science Foundation of China (51407050).


  1. 1.
    Shen, Y., Ding, S.X., Haghani, A., et al.: A comparison study of basic data-driven fault diagnosis and process monitoring methods on the benchmark Tennessee Eastman process. J. Process Control 22(9), 1567–1581 (2012)Google Scholar
  2. 2.
    Efendiev, Y., Galvis, J., Lazarov, R., et al.: Robust domain decomposition preconditioners for abstract symmetric positive definite bilinear forms. Esaim Math. Model. Numer. Anal. 46(5), 1175–1199 (2011)Google Scholar
  3. 3.
    Taieb, S.B., Bontempi, G., Atiya, A.F., et al.: A review and comparison of strategies for multi-step ahead time series forecasting based on the NN5 forecasting competition. Expert Syst. Appl. 39(8), 7067–7083 (2011)Google Scholar
  4. 4.
    Altmann, Y., Dobigeon, N., Tourneret, J.Y.: Nonlinearity detection in hyperspectral images using a polynomial post-nonlinear mixing model. IEEE Trans. Image Process. 22(4), 1267–1276 (2013)Google Scholar
  5. 5.
    Bosch, J., Kay, D., Stoll, M., et al.: Fast solvers for cahn-hilliard inpainting. Siam J. Imaging Sci. 7(1), 67–97 (2014)Google Scholar
  6. 6.
    Cui, T., Law, K.J.H., Marzouk, Y.M.: Dimension-independent likelihood-informed MCMC. J. Comput. Phys. 304(C), 109–137 (2015)Google Scholar
  7. 7.
    Dauge, M., Lafranche, Y., Raymond, N.: Quantum waveguides with corners. Esaim Proc. 35, 14–45 (2011)Google Scholar
  8. 8.
    Ding, F., Wang, X., Chen, Q., et al.: Recursive least squares parameter estimation for a class of output nonlinear systems based on the model decomposition. Circuit. Syst. Signal Process. 35(9), 1–16 (2016)Google Scholar
  9. 9.
    Dong, J., Curtmola, R., Nitarotaru, C., et al.: Pollution attacks and defenses in wireless interflow network coding systems. IEEE Trans. Dependable Secure Comput. 9(5), 741–755 (2012)Google Scholar
  10. 10.
    Burazerovic, D., Heylen, R., Geens, B., et al.: Detecting the adjacency effect in hyperspectral imagery with spectral unmixing techniques. IEEE J. Sel. Top. Appl. Earth Obs. Remote Sens. 6(3), 1070–1078 (2013)Google Scholar
  11. 11.
    Bouc, S., Stancu, R., Thévenaz, J.: Simple biset functors and double Burnside ring. J. Pure Appl. Algebra 217(3), 546–566 (2012)Google Scholar
  12. 12.
    Liu, S., Cai, C., Zhu, Q., Arunkumar, N.: A study of software pools for seismogeology-related software based on the Docker technique. Int. J. Comput. Appl. (2017). Google Scholar
  13. 13.
    Hamza, Rafik, Muhammad, Khan, Arunkumar, N., Ramírez González, G.: Hash based encryption for keyframes of diagnostic hysteroscopy. IEEE Access (2017). Google Scholar
  14. 14.
    Fernandes, S.L., Gurupur, V.P., Sunder, N.R., Arunkumar, N., Kadry, S.: A novel nonintrusive decision support approach for heart rate measurement. Pattern Recognit. Lett. (2017).
  15. 15.
    Arunkumar, N., Ramkumar, K., Venkatraman, V., Abdulhay, E., Fernandes, S.L., Kadry, S., Segal, S.: Classification of focal and non focal EEG using entropies. Pattern Recognit. Lett. 94, 112–117 (2017)Google Scholar
  16. 16.
    Arunkumar, N., Kumar, K.R., Venkataraman, V.: Automatic detection of epileptic seizures using new entropy measures. J. Med. Imaging Health Inf. 6(3), 724–730 (2016)Google Scholar
  17. 17.
    Arunkumar, N., Ram Kumar, K., Venkataraman, V.: Automatic detection of epileptic seizures using permutation entropy, Tsallis entropy and Kolmogorov complexity. J. Med. Imaging Health Inf. 6(2), 526–531 (2016)Google Scholar

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© Springer Science+Business Media, LLC, part of Springer Nature 2018

Authors and Affiliations

  1. 1.Harbin University of Science and TechnologyHarbinChina
  2. 2.Northeast Dianli UniversityJilinChina

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