Affiliations: [a]
Electric Power Research Institute, State Grid Hubei Electric Power Corporation Limited, Wuhan Hubei, China
| [b]
Wuhan DaYang YiTian Technology Co., Ltd, Wuhan Hubei, China
Correspondence:
[*]
Corresponding author. Danhui Hu, Electric Power Research Institute, State Grid Hubei Electric Power Corporation Limited, Wuhan Hubei 430077, China. E-mail: [email protected].
Abstract: Considering that the operation of power transmission and transformation equipment is not timely discovered due to the untimely data integration, a multi-dimensional heterogeneous data clustering algorithm for power transmission and transformation equipment based on multimodal deep learning is proposed. The multi-modal deep learning method is used to mine relevant data and measure the similarity between the data, which can improve the accuracy of subsequent multi-bit heterogeneous data clustering of power transmission and transformation equipment. Set up a clustering center and process data clustering to complete multi-dimensional heterogeneous data clustering of power transmission and transformation equipment. The experimental results show that the method has high clustering accuracy in the clustering of voltage deviation overrun times, voltage harmonic total distortion rate overrun times, and voltage flicker overrun times.
Keywords: Multimodal deep learning, power transmission and transformation equipment, heterogeneous data, clustering, mining, similarity
