Baozeng Li

Papers

Total Citations

H-Index

About

Baozeng Li is a leading researcher in high-voltage insulation diagnostics, specializing in partial discharge (PD) pattern recognition for gas-insulated switchgear (GIS). His work centers on advancing the reliability of power systems through intelligent condition monitoring. Li’s most notable contribution is the development of a multi-feature information fusion method for PD pattern recognition using phase-resolved partial discharge (PRPD) images, published in 2022. This approach overcomes the limitations of traditional single-feature extraction techniques, significantly improving recognition accuracy for GIS insulation state evaluation. The paper has garnered 24 citations, reflecting its impact on the field. Li’s research bridges the gap between signal processing and deep learning, offering a robust framework for real-time fault detection in high-voltage equipment. His achievements underscore a commitment to enhancing power grid safety and efficiency, making his work essential for engineers and researchers focused on asset management, predictive maintenance, and the integration of AI in electrical insulation diagnostics.