GIS equipment deformation detection based on improved ICP

Aiming at the deformation problem of gas-insulated metal-enclosed switchgear (GIS) due to various factors during the operation of the power grid, a deformation monitoring method based on iterative closest point (ICP) algorithm is proposed to achieve real time and accurate monitoring of equipment status and ensure normal production and safe operation. An improved ICP method is introduced to enhance the detection of equipment deformation, where the objective function is modified to minimize the impact of local surface features during point cloud registration. Additionally, principal component analysis (PCA) is used to extract the main feature points, reducing the volume of point cloud data. A bidirectional K-Dimensional Tree (KD-Tree) is employed to search for corresponding points, which helps to eliminate noise from inconsistent regions, thus improving both registration accuracy and efficiency. Compared to the classic algorithm, the improved method can align partially overlapping point clouds, and experiments confirm the effectiveness and robustness of the proposed approach.