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Multivariable bilinear subspace recursive likelihood identification for pumped storage motor

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Abstract

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.

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Acknowledgements

National Natural Science Foundation of China (51407050).

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Authors and Affiliations

  1. Harbin University of Science and Technology, Harbin, 150080, Heilongjiang, China

    Zhuang Xu, Ge Bao-Jun & Tao Dajun

  2. Northeast Dianli University, Jilin, 132012, China

    Zhuang Xu

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  1. Zhuang Xu
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  2. Ge Bao-Jun
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Correspondence to Zhuang Xu.

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Xu, Z., Bao-Jun, G. & Dajun, T. Multivariable bilinear subspace recursive likelihood identification for pumped storage motor. Cluster Comput 22 (Suppl 2), 4527–4533 (2019). https://doi.org/10.1007/s10586-018-2066-6

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  • DOI: https://doi.org/10.1007/s10586-018-2066-6

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