Separation and Classification of Corona Discharges Under Low Pressures Based on Deep Learning Method

Review Engineering, Electrical & Electronic

Insulation Materials and Systems for Power Electronics Modules: A Review Identifying Challenges and Future Research Needs

Boya Zhang et al.

Summary: This critical review examines recent research on electrical insulation materials and systems in power electronics devices, focusing on electrical treeing in silicone gel, PD modeling, and mitigation methods. The review identifies drawbacks and technical gaps in the reported research, particularly noting that investigations targeting the working conditions of next-generation WBG power devices are still in their infancy. The review will serve as a useful framework and reference point for future research.

IEEE TRANSACTIONS ON DIELECTRICS AND ELECTRICAL INSULATION (2021)

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Article Physics, Fluids & Plasmas

A Deep Learning Approach for Discrimination of Single- and Multi-Source Corona Discharges

Moein Borghei et al.

Summary: Insulation system health is crucial for the reliable operation of electrical apparatus, and this study uses deep learning algorithms to develop an online condition monitoring system capable of distinguishing single- and multi-source corona discharges. Through experimental testing and data conversion into discharge images, a ResNet model was optimized to achieve the highest accuracy with the lowest computational cost.

IEEE TRANSACTIONS ON PLASMA SCIENCE (2021)

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Article Biotechnology & Applied Microbiology