用加性高斯过程建模风电场功率，捕捉机组与场站级差异。
On Additive Gaussian Processes for Wind Farm Power Prediction
- 构建加性高斯过程，分离机组与场站级功率特征
- 揭示风场功率变化规律，符合物理直觉
- 适合风电运维与智能控制决策者参考
群体结构健康监测（PBSHM）旨在共享相似机器或结构间的信息。本文从群体层面出发，利用加性高斯过程分析收集的风场数据，揭示涡轮机特异性和场站级功率模型的变化。预测结果展现出符合直觉的风场功率生成模式，有助于实现更科学的控制与决策。
原文摘要 · Abstract (English)
Population-based Structural Health Monitoring (PBSHM) aims to share information between similar machines or structures. This paper takes a population-level perspective, exploring the use of additive Gaussian processes to reveal variations in turbine-specific and farm-level power models over a collected wind farm dataset. The predictions illustrate patterns in wind farm power generation, which follow intuition and should enable more informed control and decision-making.
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