现代电力2024，Vol.41Issue(2)：201-208,8.DOI:10.19725/j.cnki.1007-2322.2022.0217
双馈风电机组传动系统神经网络建模及参数预测
Neural Network Modelling and Parameter Prediction of Drive Train in a DFIG Wind Turbine
摘要
Abstract
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.关键词
双馈风电机组/参数预测/功率谱特征/可辨识性/BP神经网络Key words
DFIG WT/parameter prediction/power spec-trum characteristics/identifiability analysis/BP neural net-work分类
信息技术与安全科学引用本文复制引用
丁新虎,潘学萍,孙晓荣,和大壮,陈海东..双馈风电机组传动系统神经网络建模及参数预测[J].现代电力,2024,41(2):201-208,8.基金项目
国家自然科学基金资助项目(52077061).Project Supported by the National Natural Science Foundation of China(52077061). （52077061）
