Absence of fault samples of power equipment is a big bottleneck that limits the development of diagnostic method. This paper proposes a Space Hybridization Theory for dealing with diagnostic data insufficiency. First, the experimental datasets of transformer winding failure are obtained, constituting the real space. Then, the transformer digital space models are constructed similarly to obtain the simulation datasets, constituting the virtual space. After that, the features of the samples in two spaces are extracted through windowing feature calculation, and the real-virtual space is obtained through feature hybridization. The final samples in the space are taken as the auxiliary dataset during the intelligent diagnosis process. The diagnostic accuracy with the proposed method increased by several percent than without. While the MAPE between the real space and the virtual space is less than 9.4%, it can comprehensively improve the diagnostic effect.

电力设备故障样本的缺乏是制约诊断方法发展的一大瓶颈。本文提出了一种用于诊断数据不足的空间杂交理论。首先，获得了变压器绕组故障的实验数据集，构成了真实空间。然后，以类似的方式构建变压器数字空间模型，以获得构成虚拟空间的仿真数据集。然后，通过开窗特征计算提取两个空间中样本的特征，并通过特征杂交获得真实-虚拟空间。在智能诊断过程中，将空间中的最终样本作为辅助数据集。提出的方法的诊断准确性比没有提出的方法提高了百分之几。