A Detection Method for Open-Close States of High-Voltage Disconnector in Smoky Environments

Computer vision-based state recognition is widely employed in substations, but conventional video monitoring systems often encounter challenges during emergency situations, such as smoke from fires. In such scenarios, LiDAR emerges as an appealing alternative, capable of capturing the depth information of the target. However, when smoke concentration is high, the quality of collected point cloud data deteriorates, impacting the assessment of the disconnector open-close status. This paper delves into the impact of a smoky environment on point cloud data and introduces a two-stage discrimination process. Firstly, a feature extraction method using sliced point clouds is employed to construct edge features of the conductive arm. Building upon this foundation, an open-close position identification method based on edge pre-processing is employed to obtain the final measurement results. Field experiments demonstrate that the proposed method effectively mitigates smoke interference and accurately determines the disconnector's open-close status with high reliability and precision. This approach could serve as a reference for the development of continuous disconnector closing state monitoring technology.