利用风电场布局信息提升缺失发电数据的补全精度
Data is missing again -- Reconstruction of power generation data using $k$-Nearest Neighbors and spectral graph theory
- 结合风电场布局与谱图理论构建动态加权图进行近邻补全
- 在西海岸粗犷海上风电场测试中效果优于不考虑布局的方法
- 适合处理大规模风电场传感器数据缺失问题
随着风力涡轮机和传感器数量增加，风电场出现数据缺失及记录不完整风险上升。本文提出一种融合数据驱动方法与专家知识的插补策略，利用风电场几何结构优化近邻插补结果。该方法基于谱图理论学习风电场图的拉普拉斯特征映射，所用图结构可仅基于布局，也可融合实测数据信息，并支持随时间动态更新，实现在线跟踪。在西海岸粗犷（Westermost Rough）海上风电场的应用表明，该方法显著优于未考虑风电场布局信息的基准方法。
原文摘要 · Abstract (English)
The risk of missing data and subsequent incomplete data records at wind farms increases with the number of turbines and sensors. We propose here an imputation method that blends data-driven concepts with expert knowledge, by using the geometry of the wind farm in order to provide better estimates when performing Nearest Neighbor imputation. Our method relies on learning Laplacian eigenmaps out of the graph of the wind farm through spectral graph theory. These learned representations can be based on the wind farm layout only, or additionally account for information provided by collected data. The related weighted graph is allowed to change with time and can be tracked in an online fashion. Application to the Westermost Rough offshore wind farm shows significant improvement over approaches that do not account for the wind farm layout information.
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