A health state prediction method of traction converter IGBT based on optimized particle filter. (December 2022)

• Record Type: Journal Article
• Authors: Shi, Yunming; Ai, Yu; Chen, Shaoyong; Zhang, Chenyang; Liu, Jianqiang
• Is Part Of: Microelectronics and reliability. Volume 139(2022)
• Publication Date: 2022-12
• Subjects: Insulated gate bipolar transistor (IGBT) -- Health state prediction -- Unscented particle filter -- Thermal resistance -- Genetic algorithm; Electronic apparatus and appliances -- Reliability -- Periodicals; Miniature electronic equipment -- Periodicals; Appareils électroniques -- Fiabilité -- Périodiques; Équipement électronique miniaturisé -- Périodiques; Electronic apparatus and appliances -- Reliability; Miniature electronic equipment; Periodicals; 621.3815
• DOI: 10.1016/j.microrel.2022.114840
• Languages: English
• ISSNs: 0026-2714

Abstract

Abstract: Insulated gate bipolar transistor (IGBT), as the most critical and vulnerable power electronic component in the field of rail transportation, its reliability has become a research hotspot in recent years. To improve the safe and reliable operation of rail transportation and to reduce the maintenance cost by rationalizing system maintenance, it is critical to monitor the health of the traction converter IGBTs. This paper first reviews the failure mechanism of the IGBT, then analyzes the health state characterization parameters of the IGBT by the accelerated aging test, selects thermal resistance as the health state characterization parameter of the IGBT, and establishes the degradation model of the IGBT. On this basis, a health state prediction method of IGBT based on the genetic algorithm unscented particle filter (GA-UPF) is proposed. This method uses an unscented Kalman filter to generate important proposal density distribution for each particle, estimates, and updates the parameters of the degradation model, alleviates the particle degradation problem, and uses a genetic algorithm to solve the particle impoverishment problem introduced by the traditional resampling method, which improves the particle diversity and prediction accuracy. Mahalanobis distance is used to monitor the abnormal state of the thermal resistance. After the abnormal occurs, the value of the thermal resistance is predicted according to the degradation model and updated parameters. The degradation trend of the thermal resistance is predicted to reflect the health state of the IGBT. Experiments on IGBT degradation data show that the proposed method can provide higher health state prediction accuracy than the traditional particle filter method. Research highlights: We propose a genetic algorithm unscented particle filter method to predict the health status of traction converter IGBT. The proposed method optimizes the particle degradation problem and the particle impoverishment problem. The proposed method is superior to the traditional PF method and UPF method in predict accuracy. Based on the proposed method, the health state of the IGBT module of high-speed EMUs can be predicted off-line.