Accurately identifying the fault type of an optical current transformer (optical CT) and evaluating the fault severity can provide strong support for the operation and maintenance of a direct current (DC) power system. In response to the problems that current research overlooks, the spatiotemporal features when making fault identification, which restrain the improvement of identification accuracy, and consider fault identification as an assessment of fault severity, which is unable to provide effective information for actual operation and maintenance work, this paper proposes an optical CT fault severity assessment model based on scene generation and spatiotemporal feature fusion. Firstly, a CNN-Transformer model is constructed to mine the fault characteristics in spatial and temporal dimensions by feature fusion, achieving accurate identification of fault types. Secondly, an improved synthetic minority oversampling method is adopted to generate virtual operating scenes, and the operating range under different operating states of the optical CT is statistically obtained. Finally, based on Shapley Additive Explanations (SHAP), the importance of the feature of optical CT is evaluated under different fault types. Reliant on the importance of features and operating range under different running states, the severity of the fault is assessed by quantifying the difference between the fault state and the normal state of the optical CT under the identified fault type. This study validated the effectiveness of the proposed method using actual operational data from an optical CT at a converter station in Hebei Province in China.

Accurate ultra-short-term power forecasting for distributed photovoltaic (DPV) systems is crucial for the intra-day operation of distribution networks. However, the current method based on a graph network only takes a few DPV sites as forecasted objects; when modeling a large number of DPV objects, the massive graph structure will require multiple instances of information propagation to achieve global correlation extraction. Due to the similar output characteristics of adjacent DPV sites, excessive information aggregation will lead to node features tending towards consistency, making information extraction inefficient and insufficient, which limits the improvement of forecasting accuracy. To address the issues above, this study proposes an ultra-short-term distributed PV power forecasting method considering spatiotemporal correlation. First, the DPV sites are clustered into several sub-regions in different layers considering the spatial location of DPV sites and the temporal characteristics of power output. And a hierarchical architecture is constructed from DPV sites to sub-regions based on subordinate relationship and the order of information transmission. After that, the output mode of every sub-region is dynamically described in refinement by filtering out the noise DPV sites with significant differences in outputs. Finally, by hierarchically and sequentially mining the local and global spatiotemporal correlation among output modes, the hierarchical dynamic graph convolutional network is applied to achieve the regional power forecasting. Experimental results based on data from 166 DPV sites demonstrate that the proposed HDGCN model significantly outperforms the best traditional benchmark model, reducing the Normalized Root Mean Square Error (NRMSE) by approximately 38.56% and the Normalized Mean Absolute Error (NMAE) by 33.79% in a 4 h-advance forecasting scale.