Junction temperature measurement method for IGBTs using turn-on miller plateau duration

Guo, Chunsheng; Zhang, Shiwei; Wei, Lei; Li, Hao; Wang, Sijin; Zhu, Konggang

doi:10.1007/s43236-021-00275-z

Cited by 3 publications

(2 citation statements)

References 19 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Mathematical thermal modeling of MMC is divided into two steps: 1) power loss calculation and 2) thermal estimation of the power devices. The energy loss Esw [20] by the switching of the power devices is shown in equation (15).…”

Section: ) B Mathematical Thermal Modelingmentioning

confidence: 99%

“…The power semiconductor switches are the main components of power conversion systems [11]. The semiconductor device failure accounts for 21 % of converter system failure, and the leading cause of the switches is thermal stress [12][13][14] As a result, the thermal state of the semiconductor determines the reliability and robustness of the systems [15]. The thermal stresses of the semiconductor occur in bond wire fatigue, metallization reconstruction, solder fatigue, gate oxide failure, and burnout failure [16].…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Thermal Estimation of Modular Multilevel Converter Submodule Using Deep Regression on GRU and LSTM Network

et al. 2022

View full text Add to dashboard Cite

This paper proposed a GRU/LSTM-based deep regression model for thermal estimation of modular multilevel converter submodule. The MMC is composed of many submodules with the power semiconductors such as IGBTs and MOSFETs. The switches are the main components determining the reliability of the MMCs, and the swing of junction temperature causes most switch failures in the power semiconductors. So, thermal estimation is essential to improve the reliability of the MMC systems. Thermal modeling is a regression problem of time-series data, considering various environmental conditions. The conventional models cannot reflect the complex environmental conditions due to their fixed mathematic formulas. Therefore, this paper proposes the deep regression model that can estimate the junction temperature by using the arm current of the MMC submodule. The proposed model improved the accuracy of thermal estimation by more than 7.2 times compared to the existing method. Moreover, it does not require preprocessing and takes about 4.5ms on average to process 100ms data.

show abstract

Section: ) B Mathematical Thermal Modelingmentioning

confidence: 99%