Work

GA-LSTM-Based Degradation Prediction for IGBTs in Power Electronic Systems

Year: 2025

Type: article

Abstract: The reliability and lifetime of insulated gate bipolar transistors (IGBTs) are critical to ensuring the stability and safety of power electronic systems. IGBTs are widely used in electric vehicles, renewable energy systems, and industrial automation. However, their degradation over time poses a significant risk to system performance. Therefore, this paper proposes a data-driven approach based on a Long Short-Term Memory (LSTM) network optimized by a Genetic Algorithm (GA) to predict IGBT degradation. The study examines the health monitoring of insulated gate bipolar transistors from a device physics perspective. Degradation mechanisms that alter parasitics and electro-thermal stress produce characteristic changes in the turn-off overvoltage and the on-state voltage. Using power-cycling data from packaged half-bridge modules, an LSTM-based sequence model configured by a genetic algorithm search reduces error against an identically trained baseline (RMSE = 0.0073, MAE = 0.057, MAPE = 0.726%) under the shared protocol, with the clearest advantages in the early stage of degradation. These results support predictive maintenance and health management in power-electronic systems.

Source: Energies

DOI: 10.3390/en18215574

Authors: Yunfeng Qiu, Zehong Li, Shan Tian

Institutions: University of Electronic Science and Technology of China, State Key Laboratory of Electronic Thin Films and Integrated Devices, Sichuan University, Chengdu University of Information Technology

Language: English

Topic: Silicon Carbide Semiconductor Technologies

Subfield: Electrical and Electronic Engineering

Field: Engineering

Domain: Physical Sciences

SDGs: Industry, innovation and infrastructure, Affordable and clean energy

Open Access status: gold

Estimated APC paid: $3,137