Juecen Mao
ABSTRACT
To deal with the adverse effects of multi-input and multi-disturbance on thermal plant model identification, a thermal closed-loop process identification method integrating machine learning is proposed. Firstly, the optimal method of identification data based on feature classification machine learning is used to select the historical operation data suitable for identification. The method establishes the feature construction rules of historical operation data, reduces the dimension of data and can better represent the dynamic information. The random forest is used to establish the identification data classification rule model, so as to obtain high prediction accuracy of identification model. Secondly, an identification process integrating input variable selection, model order determination and unbiased parameter estimation is proposed. Among them, the variance expansion factor method and the variable projection analysis method of partial least square regression are used to select the input variables of the identification model, and then the model parameters are unbiasedly identified based on the asymptotic identification method. The ML-combined identification method is applied to the deaerator water level system of thermal power unit, and its reliability and accuracy are verified by closed-loop simulation.
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Index Terms
- A ML-combined closed-loop identification method for thermodynamic process
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