Intensive Multiorder Feature Extraction for Incipient Fault Detection of Inverter System

Inverter systems play a crucial role in aerospace, defense, transportation, modern industry, and power systems, leading to extensive efforts from scholars and engineers in fault diagnosis. Data-based methods are widely utilized with the accessible history data instead of complex math modeling for this issue but they are incompetent for obstinate incipient fault. Therefore, this article proposes an intensive multiorder feature extractor (IMFE) for the incipient fault detection of inverter system, with intensively extracting deep statistical features and reducing harmful perturbations. First, a dense structure with short paths between nonadjacent layers is adopted for multiorder knowledge reutilization. Then, the acquired features are refined and the low-quality information is discarded. In addition, the effectiveness of IMFE is demonstrated through rigorous mathematical derivation with sensitivity and complexity analysis. Finally, a three-phase inverter system platform based on current and voltage dual control is established to verify the superiority of the proposed method. Experimental results show that the proposed approach significantly improves fault detection performance, achieving a 3.1% higher fault detection rate compared to existing state-of-the-art methods.