Abstract
Insulated gate bipolar transistor (IGBT) is widely used in power equipment, it generally works in complex circuit profiles and it is very difficult to measure or predict the thermal parameters of the module in real-time and evaluate the corresponding health status in the transient process. This paper develops a novel approach for solder-layer condition monitoring of IGBTs. In the approach a time-series nonparametric model of a power module is constructed, the current power and ambient temperature data are used to deduce the health state junction and case temperature. Three groups of time-series insulated gate bipolar transistors (IGBTs) data are used to train and verify the time-series nonparametric model for online conditions, the results show that the developed method has high accuracy. Compared with traditional methods, the time series non-parametric model method not only saves characteristic experiments but also saves the process of mathematical model construction. Besides, the proposed method also has the advantages of strong generalization and low equipment requirements which is useful for actual working conditions. Thereafter, another nonparametric model is built, the predicted junction temperature is used to estimate the collector voltage in the health state, and the percentage deviation of the measured collector voltage from the estimated voltage is used to do the state-of-health estimation of the IGBT and its accuracy is verified by the experiment result.
Highlights
Power inverters are one of the most reliability-critical parts in power electronic systems such as photovoltaic (PV) systems and wind-power generation systems
It shows that the root mean square error (RMSE) result of the time series nonparametric model and the experimental data is 0.4°C, which indicates that the time series nonparametric model has high accuracy in predicting the transient case temperature of the module
The method solves the personalized parameter prediction and real-time parameter prediction problem, and the state-of-health of the module is evaluated based on the real-time parameter prediction
Summary
Power inverters are one of the most reliability-critical parts in power electronic systems such as photovoltaic (PV) systems and wind-power generation systems. Piton et al [6] presented an experimental set-up based on optical fibers to measure IGBT chip temperatures online The drawback of this method is that it destroys the module. Ma et al [13] proposed a health monitoring method by harnessing the inverter operational characteristics and degradation-sensitive electrical parameters to address the IGBT wire bonding faults The approach obtains both the wire bonding failure features and junction temperature from the terminals of an IGBT module to do online health monitoring. Yang et al [15] proposed an online IGBT junction temperature measurement method based on the on-state voltage drop. This paper established a time-series nonparametric model under varied conditions to predict the junction temperature and do the state-of-health estimation.
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