Reliability Assessment of IGBT Through Modelling and Experimental Testing

Abstract

Lifetime of power electronic devices, in particular those used for wind turbines, is short due to the generation of thermal stresses in their switching device e.g., IGBT particularly in the case of high switching frequency. This causes premature failure of the device leading to an unreliable performance in operation. Hence, appropriate thermal assessment and implementation of associated mitigation procedure are required to put in place in order to improve the reliability of the switching device. This paper presents two case studies to demonstrate the reliability assessment of IGBT. First, a new driving strategy for operating IGBT based power inverter module is proposed to mitigate wire-bond thermal stresses. The thermal stress is characterised using finite element modelling and validated by inverter operated under different wind speeds. High-speed thermal imaging camera and dSPACE system are used for real time measurements. Reliability of switching devices is determined based on thermoelectric (electrical and/or mechanical) stresses during operations and lifetime estimation. Second, machine learning based data-driven prognostic models are developed for predicting degradation behaviour of IGBT and determining remaining useful life using degradation raw data collected from accelerated aging tests under thermal overstress condition. The durations of various phases with increasing collector-emitter voltage are determined over the device lifetime. A data set of phase durations from several IGBTs is trained to develop Neural Network (NN) and Adaptive Neuro Fuzzy Inference System (ANFIS) models, which is used to predict remaining useful life (RUL) of IGBT. Results obtained from the presented case studies would pave the path for improving the reliability of IGBTs. INDEX TERMS Reliability, power electronics, IGBT, finite element analysis, accelerated aging test, data-driven prognostics, machine learning.

Potential Fault Diagnosis Method and Classification Accuracy Detection of IGBT Device Based on Improved Single Hidden Layer Feedforward Neural Network

“…erefore, the fault diagnosis method of IGBT devices is studied, maintaining overall system stability, reducing economic losses and casualties related to employees, and is significant. e original fault diagnosis method is primarily diagnosed with the hard fault of the short-circuit open circuit in the actual circuit [6]. It is not possible to improve the defects inside the device in good reactive devices.…”

Review of Health Prognostics and Condition Monitoring of Electronic Components

“…The reliability assessment of IGBT is presented through a case study of IGBT based power inverter module, and the degradation behaviour of IGBT is analyzed through a machine learning approach. The failure of IGBT is explored at accelerated environmental parameters and mechanical stress [35]. A failure model for IGBT based photovoltaic (PV) systems based on the ageing effect is discussed.…”

A New Data-Driven Approach for Power IGBT Remaining Useful Life Estimation Based On Feature Reduction Technique and Neural Network

“…Noteworthily, these experiments were stopped when the IGBT latch-up and thermal runaway which are the end-of-life (EOL) criteria. Under the aging experiment, the collector-emitter voltage (V ce ), gate-emitter voltage (V ge ), collector-current (I c ), Thermal and electrical resistance, and switch turn On (T on ) and turn Off (T off ) and time (seconds) were measured (physical quantities) [28]. In the rest of this paper, we use the collector-emitter voltage (V ce ) as a precursor signal [29].…”