基于双源域自适应的风电机组发电机状态监测

金晓航; 赵中正; 关汉林; 刘家光; 彭一真

doi:10.19912/j.0254-0096.tynxb.2024-2283

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基于双源域自适应的风电机组发电机状态监测

更新时间：2026-07-21

- 基于双源域自适应的风电机组发电机状态监测
- 基于双源域自适应的风电机组发电机状态监测
- 太阳能学报 2026年47卷第5期页码：216-226
- 作者机构：
- 作者简介：
- 基金信息：
  
  国家重点研发计划（2022YFE0198900）;国家自然科学基金（62473336）;浙江省自然科学基金（LZ25F030004）
- DOI：10.19912/j.0254-0096.tynxb.2024-2283
  中图分类号： TH17
- 网络首发：2026-06-04，
  
  纸质出版：2026
- 稿件说明：
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金晓航, 赵中正, 关汉林, 等. 基于双源域自适应的风电机组发电机状态监测[J]. 太阳能学报, 2026,47(5):216-226.

金晓航, 赵中正, 关汉林, et al. 基于双源域自适应的风电机组发电机状态监测[J]. 2026, 47(5): 216-226.
金晓航, 赵中正, 关汉林, 等. 基于双源域自适应的风电机组发电机状态监测[J]. 太阳能学报, 2026,47(5):216-226. DOI： 10.19912/j.0254-0096.tynxb.2024-2283.

金晓航, 赵中正, 关汉林, et al. 基于双源域自适应的风电机组发电机状态监测[J]. 2026, 47(5): 216-226. DOI： 10.19912/j.0254-0096.tynxb.2024-2283.

摘要

针对新装机组数据量不足和单源域信息有限的问题

提出一种基于双源域自适应的风电机组状态监测方法

通过特定域的数据分布与回归预测对齐

实现机组发电功率的准确预测。首先

通过皮尔逊系数选出与机组发电机相关的特征;其次

通过公共特征提取器提取出目标域和源域机组的公共特征;然后

通过域特定特征对齐与域特定回归器对齐

减少源域和目标域机组的特征分布差异并减少目标域机组经过不同源域机组训练回归器的输出差异;接着

融合回归损失、域特有特征差异损失和域回归器差异损失3个损失函数

联合优化模型的总损失;最后

将目标域机组在线数据输入到模型中预测发电功率

结合功率预测残差分析对机组运行状态进行监测与判断。结果表明所提方法比单源域自适应具有更高的预测精度

可有效实现风电机组的运行状态监测。

Abstract

A wind turbine condition monitoring method based on dual-source domain adaptation is proposed to address the challenges of insufficient data for newly installed turbines and limited data from a single source domain. This method enhances the accuracy of wind turbine power prediction by employing a two-stage framework for domain domain-specific alignment and domain-specific regression prediction alignment. First

features correlated with the turbine generator are selected using the Pearson correlation coefficient. Then

features from both the target and source domain turbines are extracted via a common feature extractor. Domain-specific feature alignment and domain-specific regressor alignment are applied to reduce the feature distribution discrepancy between the source and target domain turbines

as well as to minimize the output differences of regressors that are trained on different source domains for the target turbine. Subsequently

the total loss of the model is jointly optimized by integrating three loss functions： regression loss

domain-specific feature difference loss and domain regressor difference loss. Finally

the model is used to predict the power of the target domain turbine using online data

and the turbine's operational conditions are monitored and evaluated through the analysis of prediction residuals. The results show that the proposed method outperforms single-source domain adaptation in prediction accuracy and enables wind turbine condition monitoring effectively.

关键词

Keywords

references

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