A Mechanism-Guided Intelligent Enhancement Method for Early Aging Information of Winding Insulation

Early aging diagnosis of winding insulation is crucial for preventing insulation failures. However, early aging suffers from unclear mechanisms and weak changes of electrical parameters, which makes it difficult for mechanism and intelligent diagnosis approaches to achieve effective modeling. To address this issue, this article proposes a mechanism-guided intelligent enhancement method for early aging information based on the common-mode impedance spectrum. First, an effective mechanism knowledge focusing strategy is proposed, which can effectively alleviate coupling of aging mechanism features under different aging types, so as to provide the pure mechanism features for the dynamic knowledge matching. Then, a dynamic knowledge matching strategy is presented, by which aging mechanism features and early aging information are reliably matched, thereby supporting the strong generalization of the mechanism-guided reconstruction. Finally, a mechanism-guided reconstruction network is constructed to enhance the insulation early aging information, so as to achieve high accuracy and strong-generalization diagnosis modeling of insulation early aging. Notably, our method provides a promising solution for the diagnosis modeling of early aging. The experimental results on a 4-kW permanent magnet synchronous motor drive system demonstrate that our work outperforms the existing methods, which achieves the 60.74% reduction in the early aging diagnosis error of winding insulation.