Reinforcement Learning-Based Genetic Algorithm for Aging State Analysis of Insulating Paper at Transformer Hotspot.

The aging state evaluation of insulating paper at the transformer hotspot is a pain point in the industry. To address this issue, the modified dielectric response (MDR) model and the reinforcement learning-based genetic algorithm (RLGA) are proposed to analyze the aging state of the insulating paper at the hotspot. First, the aging state-related polarization and depolarization currents (PDC) of transformer insulation are collected. Then, the MDR model is reported to describe the PDC property of the insulating paper. The RLGA is later proposed to search the optimal model parameters defined in MDR to characterize the aging state of the insulating paper at the hotspot. Verification results present the feasibility and validity of the extracted optimal model parameters for aging analysis. Furthermore, the quantitative correlation between these parameters and the aging state of the hotspot is analyzed. Regarding this, the present work provides a potential method for obtaining aging information on the insulating paper at the hotspot.