Insulation Defect Lifecycle Prediction of Switchgear Based on Multi-Parameter MKL Model

The development of detection techniques for insulation defects in switchgear is relatively mature. However, there remain numerous challenges in intelligently devising maintenance plans for switchgear based on diverse detection data. Therefore, the construction of a lifecycle prediction model for insulation defects in switchgear is of significant theoretical and practical value for exploring the development trends of insulation defects, achieving status-based maintenance of switchgear insulation, and ensuring the safe operation of switchgear. This paper designs a lifecycle prediction model for switchgear defects based on a multi-parameter MKL model, including the processes of multi-parameter data feature extraction, data feature fusion, and defect lifecycle prediction. Firstly, aggregate features are extracted from the multi-parameter data of switchgear equipment. Subsequently, the kernel weights of various perspectives are obtained based on the MKL optimization algorithm. Finally, the SVM algorithm is employed to project the multi-perspective kernel weights into the target space, thereby obtaining the lifecycle prediction model for switchgear insulation defects. Through the analysis of experimental data and application cases, the proposed method effectively integrates various detection data of switchgear, accurately identifies the development trends of switchgear insulation defects, and demonstrates good robustness and high accuracy in evaluation results. It can be applied to trend prediction of insulation defects and insulation status detection in switchgear equipment in various environments.