As a kind of renewable energy with economic and environmental friendliness, wind energy is widely used, although the high uncertainty of wind speed has significant impact on the operation and planning of wind power systems. Thus, scenario generation of wind speed is the primary step to obtain optimal decisions. However, as to the wind power plants to be newly-built or expanded, adequate wind data may not be available, or even the wind data are missing or invalid, which may lead to the inaccuracy of data-driven scenario generation. In this paper, considering that multiple wind plants in neighboring areas may have similar wind patterns, a novel scenario generation framework is proposed to transfer the knowledge extracted from existing data-rich plants to help establish the uncertainty model of newly-built plant. Transfer component analysis (TCA) is used to minimize the distribution gap between multiple source plants and target plant, and the results of maximum mean discrepancy (MMD) can be obtained to construct the mixture distribution model of target wind speed. Experimental results show that the scenarios generated by transferring the knowledge of existing wind plants can better reflect the real characteristics of the target wind speed in the case of insufficient historical data.

中文翻译：

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基于现有风电厂知识的新型情景生成框架

风能是一种具有经济和环境友好性的可再生能源，尽管风速的高度不确定性对风电系统的运行和规划产生了重大影响，但风能却得到了广泛的应用。因此，风速的场景生成是获得最佳决策的主要步骤。但是，对于将要新建或扩建的风力发电厂，可能没有足够的风力数据，甚至风力数据丢失或无效，这可能导致数据驱动方案生成的准确性。在本文中，考虑到邻近地区的多个风电厂可能具有相似的风型，提出了一种新颖的情景生成框架，以转移从现有数据丰富的电厂中提取的知识，以帮助建立新建电厂的不确定性模型。利用传递成分分析（TCA）可以最大程度地减少多源植物与目标植物之间的分布差异，并可以得到最大平均差异（MMD）的结果来构建目标风速的混合分布模型。实验结果表明，在历史数据不足的情况下，通过转移现有风电厂的知识而产生的情景可以更好地反映目标风速的真实特征。