Real-time multi-channel ion migration monitoring and early warning for high-voltage power modules

With the growing deployment of high-voltage power modules in rail-transit traction drives, photovoltaic (PV) inverters, and industrial power conversion systems, ensuring long-term insulation reliability under harsh electrical and environmental stress has become a critical challenge, and ion migration in insulating and packaging materials is emerging as an important degradation mechanism. However, most existing test setups are limited in voltage range, channel count, and sampling rate, and are mainly suited for offline characterization rather than real-time monitoring with early warning, which constrains system-level screening and timely detection of ion-migration-induced failures. This paper presents a real-time multi-channel ion migration monitoring and early warning method for high-voltage power modules. A high-voltage isolation front-end and protection architecture enables continuous programmable bias up to 6 kV with picoampere-level leakage measurement and 0.1 fA resolution, reproducing realistic stress conditions at the module level. On this basis, a scalable 1024-channel monitoring platform with hierarchical multiplexing and FPGA-assisted data acquisition maintains a stable sampling period of 20 ms per channel while supporting flexible, user-defined channel grouping and stress configurations. An ion-migration-aware early warning algorithm is further developed that fuses multi-timescale leakage-current trends, equivalent resistance gradients, and cross-channel anomaly scores into interpretable, graded risk indicators visualized via dashboards and heat maps. Experiments on representative high-voltage modules and dielectric coupons show that the proposed method operates stably under kilovolt-level stress, accurately tracks small leakage variations across a large number of channels, and detects pre-breakdown ion-migration signatures significantly earlier than a baseline low-channel, low-rate setup, providing a practical tool for reliability screening, qualification, and predictive maintenance.