Effective Data-driven Method Based on Bayesian Approach for Performance Estimation of Wound-field Motors

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  • Zhao Tie yang
    Department of Electrical, Electronics and Information Engineering, Nagaoka University of Technology
  • Hidaka Yuki
    Department of Electrical, Electronics and Information Engineering, Nagaoka University of Technology
  • Hiruma Shingo
    Graduate School of Engineering, Kyoto University
  • Kaimori Hiroyuki
    Science Solutions International Laboratory, Inc.

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<p>This study proposes a novel data-driven method based on Bayesian approach. A d/q axis flux map and the non-linear torque characteristics of wound-field motors were estimated using a single layer neural network to reduce the number of finite element analysis and load tests. Moreover, to improve estimation accuracy even with a small number of learning data, training data-set to be input into machine learning were selected based on a Bayesian approach. The proposed method improves the estimation accuracy compared to that of the conventional data-driven method. The proposed method is validated by applying it to the numerical and experimental problem. Moreover, estimated results are compared with those of the conventional methods.</p>

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