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Multimodal imaging inspection with intelligent inspection robots and drones has revolutionized the online monitoring and diagnosis of substation equipment. Nevertheless, such robots and drones generate a large amount of 2-D multimodal inspection images, increasing the workload for substation staff tasked with image analysis. To address this problem, we develop a 3-D reconstruction approach that integrates these 2-D images into intuitive 3-D multimodal models. Specifically, a novel 3-D multimodal reconstruction framework, leveraging the building information model (BIM) of the substation, is designed for recovering the high-fidelity 3-D multimodal models from the 2-D multimodal images of substation equipment. Central to this framework is an image decision strategy, used to accurately match different modal images. In addition, an image harmonization method is demonstrated to minimize discrepancies among the same modal images, maintaining color consistency for the texture mapping between the 3-D BIM and 2-D multimodal images. The quantitative and qualitative experiments conducted on self-built virtual and real-world substation platforms demonstrate the superiority of our method, which yields more complete and accurate 3-D multimodal models than state-of-the-art reconstruction techniques.

The incipient fault of a medium-voltage cable joint in a water-logged environment occurs before the permanent fault. Monitoring it can help prevent loss through the permanent fault. However, the characteristics of the incipient fault remain largely unknown. In this article, we report on the experimental simulation of an incipient fault according to actual operating scenarios and the measurement of both nonelectrical and electrical characteristics of the incipient fault. We then divide the incipient fault into three phases and further divide the start phase into three secondary phases, namely those relating to the predischarge, episodic discharge, and substable discharge (SSD). The dynamic characteristic of the liquid electrode in the incipient fault reveals that there is a certain stability when the SSD phase appears, which is considered the stage in which characteristics of the incipient fault can be extracted. The time-domain characteristics of the fault are somewhat unclear, while the harmonic content in the stage of the SSD is obviously clear and hardly changes with the further occurrence of incipient faults. This distinctive characteristic appears multiple times between the first incipient fault and the permanent fault and can be used to capture the appearance of incipient faults.

The porcelain insulator incipient fault (PIIF) is a self-clearing discharge process caused by water entering the crack of the porcelain insulator. It will not result in a tripping operation but may evolve into a permanent grounding fault after multiple occurrences, leading to various types of accidents, such as wire breaks, power outages, fires, and personal electric shocks. This paper uncovered the mechanisms behind the initiation and evolution of the PIIF. Unique characteristics of the PIIF are analyzed from aspects such as the starting time, the power-resistance variation trend, and the fault duration. The characteristics were verified and supplemented by field experiments, and the influence of the insulator models, neutral grounding modes, and the number of discharges on these characteristics was analyzed. The results lay the foundation for the identification and pre-warning of PIIF.

The tree-contact incipient fault (TCIF) is caused by a temporary contact between a distribution feeder conductor and a tree branch driven by the wind. Compared with permanent vegetation contact faults, TCIFs can easily escape notice due to their self-cleaning properties. But a branch can cause multiple TCIFs over significant periods of time, and also carries the risk of vegetation ignition. This paper analyzes the generation and development mechanism of TCIF and divides the fault into three stages: approach, contact, and separation. The model of equivalent grounding resistance in each stage is established, and its features in different stages are analyzed. The features of TCIF were further analyzed by field experiments. The influences of varying types of tree branches, different moving speeds, and neutral grounding modes on the fault grounding current waveform are analyzed. The results discussed in this paper establish a basis for the detection of TCIF.

Motivated by the increasing frequency and intensity of extreme weather events, this article 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.