基于神经网络的绝缘栅双极型晶体管开关损耗预测

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Abstract Aiming at the disadvantages that numerous insulated gate bipolar transistor (IGBT) switching loss are difficult to accurately measure online in the cascaded energy storage application area, switching loss prediction model is established based on the error back propagation neural network. Firstly, dynamic test system of switching loss is built with cascaded H bridge power module, the massive switching loss data is obtained with changing the direct current bus voltage, alternating current and coolant temperature of power module. 3 main factors including collector-emitter voltage, collector current and device junction temperature are taken as the input of IGBT switching loss prediction model. The particle swarm optimization is used to optimize the initial weight and threshold of prediction model, improving prediction accuracy and accelerating the convergence of learning laws. The optimized performance of this model is compared and analyzed with the prediction model that the initial weight and threshold are given randomly. The results show that the prediction accuracy of the model proposed in this paper is higher. The maximum percentage error for 50 sets of random validation data is 3.3%.

Key words： insulated gate bipolar transistor (IGBT) switching loss prediction neural network particle swarm optimization

Received: 19 September 2024

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	Articles by authors
	WANG Changhua
	LI Xiangxiong
	LIANG Shunfa
	CHEN Rongdong

Cite this article:

WANG Changhua,LI Xiangxiong,LIANG Shunfa等. Insulated gate bipolar transistor switching loss prediction based on neural network[J]. Electrical Engineering, 2025, 26(3): 42-48.

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https://dqjs.cesmedia.cn/EN/Y2025/V26/I3/42

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