考虑电网电压扰动的风电机组载荷预测方法

孙玉源; 王晓东; 付德义; 刘颖明; 杨斌

doi:10.19912/j.0254-0096.tynxb.2025-0370

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太阳能学报 ›› 2026, Vol. 47 ›› Issue (7) : 144-152. DOI: 10.19912/j.0254-0096.tynxb.2025-0370

考虑电网电压扰动的风电机组载荷预测方法

孙玉源¹, 王晓东¹, 付德义², 刘颖明¹, 杨斌¹

作者信息 +

LOAD PREDICTION METHOD FOR WIND TURBINES CONSIDERINGGRID VOLTAGE DISTURBANCES

Sun Yuyuan¹, Wang Xiaodong¹, Fu Deyi², Liu Yingming¹, Yang Bin¹

Author information +

文章历史 +

摘要

提出一种基于TCN-iTransformer的运行数据驱动风电机组关键部位时序载荷预测方法。首先对风电机组关键部位载荷及电网数据、风电机组运行数据不同测量值进行相关性分析,提取关键特征量。采用时序卷积网络突出的局部时序特征提取能力,捕捉时间序列数据中的短期依赖关系,采用iTransformer算法对变量之间的非线性复杂交互关系进行建模,从而实现风电机组关键部位载荷精准预测。基于实测数据的算例结果分析表明,TCN-iTransformer模型预测效果较Transformer、iTransformer等模型预测精度明显提高。

Abstract

In order to realize the rapid and convenient quantification of wind turbine load under voltage disturbance condition, this paper proposes a time series load prediction method for key parts of wind turbine driven by operation data based on TCN-iTransformer. Firstly, the correlation analysis of different measured quantities of load and power grid data and wind turbine operation data of key parts of the wind turbine is carried out to extract the key characteristic quantities. The outstanding local time series feature extraction ability of the temporal convolution network is used to capture the short-term dependencies in the time series data, and the iTransformer algorithm is used to model the nonlinear complex interaction between variables, so as to achieve accurate prediction of the load of key parts of the wind turbine. The analysis of the results based on the measured data shows that the prediction accuracy of the TCN-iTransformer model is significantly higher than that of the Transformer, iTransformer and other models.

导出引用

孙玉源, 王晓东, 付德义, 刘颖明, 杨斌. 考虑电网电压扰动的风电机组载荷预测方法[J]. 太阳能学报. 2026, 47(7): 144-152 https://doi.org/10.19912/j.0254-0096.tynxb.2025-0370

Sun Yuyuan, Wang Xiaodong, Fu Deyi, Liu Yingming, Yang Bin. LOAD PREDICTION METHOD FOR WIND TURBINES CONSIDERINGGRID VOLTAGE DISTURBANCES[J]. Acta Energiae Solaris Sinica. 2026, 47(7): 144-152 https://doi.org/10.19912/j.0254-0096.tynxb.2025-0370

中图分类号： TM614

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