Data mining based wind turbine operating condition monitoring method research

Zihan Zhao; Xiangjia Sun

doi:10.1117/12.2656925

13 January 2023 Data mining based wind turbine operating condition monitoring method research

Zihan Zhao, Xiangjia Sun

Proceedings Volume 12510, International Conference on Statistics, Data Science, and Computational Intelligence (CSDSCI 2022); 125100U (2023) https://doi.org/10.1117/12.2656925
Event: International Conference on Statistics, Data Science, and Computational Intelligence (CSDSCI 2022), 2022, Qingdao, China

Abstract

The wind turbine operating condition monitoring method has the problem of large errors, therefore, a data mining based wind turbine operating condition monitoring method is designed. According to the spatial position of the wind turbine shaft, the vibration signal of the wind turbine is collected, the input power of the wind turbine and the wind energy utilization coefficient are calculated, the operating condition identification model is constructed, the training data set and the test data set are obtained by using sampling, and the monitoring model is optimized based on data mining. Test results: The mean values of RMSE, MAPE and MAE for the wind turbine operating condition monitoring method in the paper are less than those of the other two wind turbine operating condition monitoring methods: 0.1289~1.3159, respectively, indicating that the overall error of the wind turbine operating condition monitoring method in the paper is smaller after making full use of data mining techniques.

Citation Download Citation

Zihan Zhao and Xiangjia Sun "Data mining based wind turbine operating condition monitoring method research", Proc. SPIE 12510, International Conference on Statistics, Data Science, and Computational Intelligence (CSDSCI 2022), 125100U (13 January 2023); https://doi.org/10.1117/12.2656925

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