Prediction Method of Driving Strategy of High-Power IGBT Module Based on MEA-BP Neural Network

Abstract

An insulated gate bipolar transistor (IGBT) driver is crucial for improving the reliability of a power electronics system. This paper proposes a method for predicting the optimal driving strategy of high-power IGBT module based on backpropagation neural network optimized by the mind evolutionary algorithm in order to solve the problem of compromise among switching loss, switching time and overshoot and achieve a good driving effect. The three regions of switching transitions are analyzed based on the switching characteristics of the IGBT module. Neural networks are established to predict turn-on and turn-off driving strategies for variable gate resistance active gate driver of the IGBT module. The mind evolutionary algorithm is used to optimize the weights and biases of the neural networks so that the optimal weights and biases can be obtained. In order to verify the effectiveness of the driving strategy prediction method proposed in this paper, experiments are carried out for 4500V/900A the IGBT module. Compared to the conventional gate driver, the predicted driving strategies reduce the turn-on energy loss, turn-on time, overcurrent, comprehensive evaluation method, turn-on delay time and tail voltage duration by 59.31%, 46.38%, 36.99%, 65.65%, 1.9 μs, 2.9 μs, respectively. It was also found that the Planar-IGBT turn-off process was rarely affected by the gate resistance. The proposed method in this paper can be used not only for the guidance of the driving strategy determination of high-power the IGBT module driver, but also for the driver circuit improvement in the design process.

Keywords

• IGBT module
• active gate driver
• MEA-BP neural network
• driving strategy optimization
• data-driven method