New Energy Empowerment Using Kernel Principal Component Analysis in Insulated Gate Bipolar Transistors Module Monitoring

At present, energy exhausted and environmental pollution are important issues, vigorously promoting new energy and improving the utilization efficiency and management level of new energy is an important way to achieve sustainable social development. Insulated gate bipolar transistors are important components in power converters and are widely used in new energy generation, new energy vehicles, high-speed rail and industrial production. However, the power module’s age is related to all aspects of its performance change, precluding the use of a single parameter to fully and accurately express the aging state. To monitor this state and evaluate the aging state, this study presents a method to analyze and process the state data of Insulated gate bipolar transistors power module aging tests using kernel principal component analysis and establishes a multi-dimensional grey model to evaluate the power module aging state. Using the temperature cycle aging test platform, the 7000 temperature cycling tests are implemented to accelerate the age of the power module to failure, the dynamic parameters of the power modules are measured after every 1000 cycles. During the accelerated aging process, the case temperature change rate, collector-emitter voltage drop Vce(SAT) and Miller platform of the gate signal of Vge are found to exhibit different variation trends at different aging stages. The result showed that multiple parameters are combined into integrated attributes to enable more accurate implementation of the state monitoring of power modules using the proposed method, which improves the status monitoring level of Insulated gate bipolar transistors modules. The proposed method is beneficial to improve the utilization efficiency and new energy source management level