Neural Network Modelling and Parameter Prediction of Drive Train in a DFIG Wind Turbine
The drive train is an important part of the doubly-fed induction generator(DFIG)wind turbine(WT),its model and parameters have vital influence on power system synchron-ous stability and frequency stability analysis.Therefore,an ac-curate drive train model is the prerequisite for studying the dy-namic characteristics of new energy power systems.In order to solve the difficulty of identifying model parameters due to in-sufficient measurement information for large disturbances,a neural network model is proposed based on the rich historical response data under random small disturbances excitation dur-ing normal operation of the unit,and the corresponding rela-tionship between the response data and model parameters is used to predict the driving system model parameters based on the current response data.Firstly,the BP neural network model-ling principle is introduced.Secondly,the power spectrum characteristic data of response signal is extracted based on a simulation system with a DFIG wind farm integrated into an in-finite system.Thirdly,the key parameters are selected based on the power spectrum sensitivity.Finally,the BP neural network model is built to reflect the nonlinear mapping between the re-sponse signal power spectrum and model parameters,then the model parameters are predicted based on trained neural net-work.The model error is also analyzed to validate the feasibil-ity of data-driven modelling method for WTs.
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