2023 Vol.6 Oct.N05

• Title: Automatic identification of wind turbine operation status based on data mining
• Name: TengFei Han
• Company: (Department of Information and Electromechanical engineering, Zhengzhou Business University, Zhengzhou 451200, China;
• Abstract:

  The current conventional fan operating condition identification method mainly uses temperature sensors to collect the temperature of fan components, which is better than the lack of effective analysis of fan operating condition characteristics, resulting in poor identification effect. In this regard, the automatic identification method of fan operation status based on data mining is proposed. Firstly, the data mining technology is combined with the cleaning and format processing of the fan operation status data to improve the uniformity of the data set. The fractal dimensional features and multiple fractal features of the wind turbine are analyzed by combining with regularization functions, and finally two feature parameters, gearbox oil temperature and main shaft temperature, are introduced to identify the state of the wind turbine under the pending wind condition. In the experiments, the proposed method is validated for the discrimination effect. Finally, the results from the functional tests show that the proposed method has a low power prediction error value and has a more desirable discrimination effect when the wind turbine operating state is identified by the proposed method.

  
  

• Keyword: data mining; wind turbine; operating condition; power output;
• DOI: 10.12250/jpciams2023090714
• Citation form: TengFei Han.Automatic identification of wind turbine operation status based on data mining [J]. Computer Informatization and Mechanical System,2023,Vol.6,pp.63-67

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