基于超限学习机的风电机组齿轮箱油温趋势预测研究

Study on Oil Temperature Trend Prediction of Gearbox of Wind Turbine Based on Extreme Learning Machine

为减少风电机组齿轮箱故障,提出一种基于数据预处理的超限学习机(ELM)风电机组齿轮箱油温预测方法。鉴于SCADA系统中数据信息质量的不确定性和冗余性,首先通过平滑处理去除噪声,再由相关性分析和信息熵计算,得到复合要求的模型输入变量数据,此数据预处理方法确保了对最终预测贡献的确定性,最后利用ELM预测模型进行齿轮箱油温预测。通过预测结果分析对比,此模型在保证预测精度的同时大幅降低了计算的复杂度,验证了所提算法的优越性。

An oil temperature prediction method for the wind turbine gearbox of the extreme learning machine(ELM)based on data preprocessing is proposed to reduce the fault of the wind turbine gearbox.Firstly,the noise is removed by smoothing,and then the correlation analysis and information entropy calculation are performed to obtain the model input variable data that meets the requirements in view of the uncertainty and redundancy of the data information quality in the SCADA system.This data pre-processing method ensures the certainty of the final prediction contribution.Finally,the ELM prediction model is used for Oil temperature prediction of the gearbox.Through the analysis and comparison of prediction results,this model greatly reduces the computational complexity while ensuring the prediction accuracy,and verifies the superiority of the proposed algorithm.