Rapid warning of wind turbine blade icing based on MIV-tSNE-RNN
Rapid warning of wind turbine blade icing based on MIV-tSNE-RNN
复制标题
基于MIV-tSNE-RNN的风电机组叶片结冰快速预警
DOI：
10.1007/s12206-021-1116-9
复制
发表时间：
2021-12-01
影响因子：
1.6
通讯作者：
Du, Wenliang
中科院分区：
文献类型：
--
作者：
Zhang, Zhiqiang;Fan, Bin;Du, Wenliang
提出了一种基于RNN模型的风电叶片结冰快速预警算法。通过风力机叶片历史数据和标签作为模型输入，通过平均影响值（MIV）指标对原始m维数据进行评价，剔除MIV指标小于1的数据；剩余n维数据通过tSNE方法降维到x维；将量纲数据输入到RNN中，模型输出为未来某一时期风力机叶片的结冰状态。基于某风场的SCADA数据，通过算例对模型进行了验证。以某实例为例，当模型训练数据为104个数量级时，采用MIV-tSNE-RNN算法，预测精度可达72%左右；与RNN模型相比，该模型的预测精度提高了约150%，算法运行时间减少了约45%。当数据量超过104个数量级时，使用MIV-tSNE-RNN算法，预测精度提高约100%。该算法可以根据实际需要提供准确、快速的风电叶片结冰预测结果。
A fast early warning algorithm for wind turbine blade icing based on a RNN model is proposed. Through wind turbine blade history data and labels as model input, the evaluation of raw m-dimension data through mean impact value (MIV) indices eliminates data with an MIV index of less than one; the remaining n-dimension data is reduced to x-dimension by the tSNE method; dimensional data is inputted into the RNN, and the model output is the icing state of the wind turbine blade in a certain future period. Based on the SCADA data from a wind field, the model was verified by an example. Using a certain example case, if the model training data is 104orders of magnitude, using the MIV-tSNE-RNN algorithm, the prediction accuracy can reach approximately 72 %; compared with the RNN model, the prediction accuracy is improved by approximately 150 % while reducing the algorithm running time by approximately 45 %. If the amount of data exceeds 104orders of magnitude, using the MIV-tSNE-RNN algorithm, the prediction accuracy is improved by approximately 100 %. This algorithm can provide accurate and rapid prediction results for wind turbine blade icing according to actual needs.