Intelligent Insulation Testing and Optimization Based on Machine Learning

To address the demand for high-precision insulation testing in modern complex cable networks, this study proposes and implements an intelligent insulation testing system based on FPGA technology. The system integrates decision tree (DT) models to enable efficient anomaly detection and process optimization. Utilizing the voltage-divider principle for insulation testing, the system extracts features such as maximum values, minimum values, and entropy to construct an explainable classification model capable of accurately monitoring different types of currents (leakage, absorption, and capacitive currents) during their decay processes. An adaptive post-processing filtering method is introduced to enhance classification accuracy and optimize testing efficiency by minimizing redundant range switching. Experimental results demonstrate that the proposed system achieves exceptional recognition accuracy and process stability across a wide range of resistances, significantly advancing the intelligence of insulation testing while improving measurement efficiency by 54.71%. This innovative solution provides a robust approach for high-demand electrical performance assessments.