Faulted line detection is a key step of intelligent fault diagnosis of distribution systems, laying the foundation for the further fault location and service restoration. A novel single line-to-ground (SLG) faulted line detection method based on the feature fusion framework is proposed. In the proposed framework, one-dimensional convolutional neural network is employed as a powerful tool to extract more effective features. In addition, there is an imbalance phenomenon between data of the faulted line and healthy lines when a data-driven model is used in the faulted line detection. The proposed framework offers an avenue for overcoming it and improves the accuracy of detection. Considering the limited data of SLG faults in actual power systems, prior knowledge of SLG fault detection is integrated into the data-driven model, which proves useful in reducing dependence on the training data quantity. The experiments verified the superior performance of the proposed feature fusion framework-based method.

The reliability of distribution systems is often challenged under unfavorable weather conditions, where weather-related failures occur with high probability. Predicting the number of weather-related failures in distribution systems can provide guiding information for operation and maintenance decisions, improving the risk management capability of utility companies. This paper proposes a novel Bayesian Neural Network (BNN) based model to predict weather-related failures caused by wind, rain and lightning. Superior prediction performance of the BNN based model is verified by contrast experiments with other advanced prediction models under four different evaluation metrics. BNN based prediction model presents remarkable robustness, especially in the prediction of high failure levels. In addition, compared to most previous used prediction models without any prediction confidence feedback, BNN based prediction model has the capability of uncertainty estimation. The confidence interval of prediction results can be obtained, which provides sufficient information for guiding risk management of utility companies. An effective operation and maintenance guiding scheme based on the analysis of prediction uncertainty is proposed, which fully excavates the interpretability of the proposed model and enrich the application value of the model.

Distribution of network repair scheduling after disasters (DNRSD) is important for developing a reasonable repair strategy and improving the efficiency of distribution of network repair after disasters. This study considers two cooperative relationships in the repair model, namely, simultaneous cooperation (SC) and ordered cooperation (OC), and proposes a new post-disaster distribution network repair model, the multiple cooperation model (MCM). Compared with conventional modeling, MCM can help to adjust repair strategies and prevent repair strategies from being delayed while in implementation. The original problem is divided into two stages, and collaboratively solved via dynamic programming. Compared with conventional solving methods, the proposed two-stage dynamic programming (TDP) optimization method can obtain global optimal solutions and make more reasonable repair strategies. Test cases based on the modified IEEE 123-bus and 8500-bus distribution systems verify the advantages of our proposed modeling and optimization method.

Motivated by the increasing frequency and intensity of extreme weather events, this paper aims to effectively assess the scope of zones damaged by natural disasters in distribution systems, which can help for rapid restoration of power supply. Multiple-branch faults and the uncertainty of monitoring information pose huge challenges to Disaster Damage Assessment (DDA). This paper proposes a multiple hypothesis based DDA method to solve this problem. In the proposed method, the novel external environment information is used by updating the objective function with Pre-disaster Prediction Matching Index (PPMI). The reasonable weighting factor of PPMI is theoretically derived. Multiple disaster scenarios are simulated and the DDA results show that the proposed method is more effective. The introduction of PPMI can help to rectify the assessment of zones in high disaster risk zones, thereby improving the overall performance of DDA. Therefore, the proposed DDA method breaks through the assessment bottleneck under limited internal monitoring information. In addition, new technology is proposed to speed up the DDA by setting an effective initial value of optimization, making DDA more efficient.