Reliability Assessment of IGBT through Modelling and Experimental Testing

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

The Application of Approximate Entropy Theory in Defects Detecting of IGBT Module

Defect is one of the key factors in reducing the reliability of the insulated gate bipolar transistor (IGBT) module, so developing the diagnostic method for defects inside the IGBT module is an important measure to avoid catastrophic failure and improves the reliability of power electronic converters. For this reason, a novel diagnostic method based on the approximate entropy (ApEn) theory is presented in this paper, which can provide statistical diagnosis and allow the operator to replace defective IGBT modules timely. The proposed method is achieved by analyzing the cross ApEn of the gate voltages before and after the occurring of defects. Due to the local damage caused by aging, the intrinsic parasitic parameters of packaging materials or silicon chips inside the IGBT module such as parasitic inductances and capacitances may change over time, which will make remarkable variation in the gate voltage. That is to say the gate voltage is close coupled with the defects. Therefore, the variation is quantified and used as a precursor parameter to evaluate the health status of the IGBT module. Experimental results validate the correctness of the proposed method

FAULT DETECTION AND PROGNOSTICS OF INSULATED GATE BIPOLAR TRANSISTOR (IGBT) USING A K-NEAREST NEIGHBOR CLASSIFICATION ALGORITHM

Insulated Gate Bipolar Transistor (IGBT) is a power semiconductor device commonly used in medium to high power applications from household appliances, automotive, and renewable energy. Health assessment of IGBT under field use is of interest due to costly system downtime that may be associated with IGBT failures. Conventional reliability approaches were shown by experimental data to suffer from large uncertainties when predicting IGBT lifetimes, partly due to their inability to adapt to varying loading conditions and part-to-part differences. This study developed a data-driven prognostic method to individually assess IGBT health based on operating data obtained from run-to-failure experiments. IGBT health was classified into healthy and faulty using a K-Nearest Neighbor Centroid Distance classification algorithm. A feature weight optimization method was developed to determine the influence of each feature toward classifying IGBT's health states

Application of coupled electro-thermal and physics-of-failure-based analysis to the design of accelerated life tests for power modules

In the reliability theme a central activity is to investigate, characterize and understand the contributory wear-out and overstress mechanisms to meet through-life reliability targets. For power modules, it is critical to understand the response of typical wear-out mechanisms, for example wire-bond lifting and solder degradation, to in-service environmental and load-induced thermal cycling. This paper presents the use of a reduced-order thermal model coupled with physics-of-failure-based life models to quantify the wear-out rates and life consumption for the dominant failure mechanisms under prospective in-service and qualification test conditions. When applied in the design of accelerated life and qualification tests it can be used to design tests that separate the failure mechanisms (e.g. wire-bond and substrate-solder) and provide predictions of conditions that yield a minimum elapsed test time. The combined approach provides a useful tool for reliability assessment and estimation of remaining useful life which can be used at the design stage or in-service. An example case study shows that it is possible to determine the actual power cycling frequency for which failure occurs in the shortest elapsed time. The results demonstrate that bond-wire degradation is the dominant failure mechanism for all power cycling conditions whereas substrate-solder failure dominates for externally applied (ambient or passive) thermal cycling

Asset management strategies for power electronic converters in transmission networks: Application to HVdc and FACTS devices

The urgency for an increased capacity boost bounded by enhanced reliability and sustainability through operating cost reduction has become the major objective of electric utilities worldwide. Power electronics have contributed to this goal for decades by providing additional flexibility and controllability to the power systems. Among power electronic based assets, high-voltage dc (HVdc) transmission systems and flexible ac transmission systems (FACTS) controllers have played a substantial role on sustainable grid infrastructure. Recent advancements in power semiconductor devices, in particular in voltage source converter based technology, have facilitated the widespread application of HVdc systems and FACTS devices in transmission networks. Converters with larger power ratings and higher number of switches have been increasingly deployed for bulk power transfer and large scale renewable integration—increasing the need of managing power converter assets optimally and in an efficient way. To this end, this paper reviews the state-of-the-art of asset management strategies in the power industry and indicates the research challenges associated with the management of high power converter assets. Emphasis is made on the following aspects: condition monitoring, maintenance policies, and ageing and failure mechanisms. Within this context, the use of a physics-of-failure based assessment for the life-cycle management of power converter assets is introduced and discussed

Degradation modeling and degradation-aware control of power electronic systems

The power electronics market is valued at $23.25 billion in 2019 and is projected to reach$ 36.64 billion by 2027. Power electronic systems (PES) have been extensively used in a wide range of critical applications, including automotive, renewable energy, industrial variable-frequency drive, etc. Thus, the PESs\u27 reliability and robustness are immensely important for the smooth operation of mission-critical applications. Power semiconductor switches are one of the most vulnerable components in the PES. The vulnerability of these switches impacts the reliability and robustness of the PES. Thus, switch-health monitoring and prognosis are critical for avoiding unexpected shutdowns and preventing catastrophic failures. The importance of the prognosis study increases dramatically with the growing popularity of the next-generation power semiconductor switches, wide bandgap switches. These switches show immense promise in the high-power high-frequency operations due to their higher breakdown voltage and lower switch loss. But their wide adaptation is limited by the inadequate reliability study. A thorough prognosis study comprising switch degradation modeling, remaining useful life (RUL) estimation, and degradation-aware controller development, is important to enhance the PESs\u27 robustness, especially with wide bandgap switches. In this dissertation, three studies are conducted to achieve these objectives- 1) Insulated Gate Bipolar Transistor (IGBT) degradation modeling and RUL estimation, 2) cascode Gallium Nitride (GaN) Field-Effect Transistor (FET) degradation modeling and RUL estimation, and 3) Degradation-aware controller design for a PES, solid-state transformer (SST). The first two studies have addressed the significant variation in RUL estimation and proposed degradation identification methods for IGBT and cascode GaN FET. In the third study, a system-level integration of the switch degradation model is implemented in the SST. The insight into the switch\u27s degradation pattern from the first two studies is integrated into developing a degradation-aware controller for the SST. State-of-the-art controllers do not consider the switch degradation that results in premature system failure. The proposed low-complexity degradation-aware and adaptive SST controller ensures optimal degradation-aware power transfer and robust operation over the lifetime