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Over the last two decades, various models have been developed to assess and improve the reliability of power electronic conversion systems (PECs) with a focus on those used for wind turbines. However, only few studies have dealt with mitigating the PECs thermo-mechanical effects on their reliability taking into account variations in wind characteristics. This work critically investigates this issue and attempts to offer a mitigating technique by, first, developing realistic full scale (FS) and partial scale (PS) induction generator models combined with two level back-to-back PECs. Subsequently, deriving a driving algorithm, which reduces PEC's operating temperature by controlling its switching patterns. The developed switching procedure ensures minimum temperature fluctuations by adapting the variable DC link and system's frequency of operation. It was found for both FS and PS topologies, that the generator side converters have higher mean junction temperatures where the grid side ones have more fluctuations on their thermal profile. The FS and PS cycling temperatures were reduced by 12 °C and 5 °C, respectively. Moreover, this led to a significant improvement in stress; approximately 27 MPa stress reduction for the FS induction generator PEC.

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.

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Abstract:In photovoltaic solar energy systems, power management algorithms (PMAs), usually called maximum power point tracking (MPPT) algorithms, are widely used for extracting maximum available power at every point in time. However, tracking the maximum power has negative effects on the availability of solar energy systems. This is due, mainly, to the created disturbances and thermal stresses on the associated power electronic converters (PECs). This work investigates the effects of PMA on the lifetime consumption, thermal stresses and failures on DC-DC converters used in solar systems. Firstly theoretical analysis and modelling of photovoltaic solar systems including converter's electro thermal characteristics were developed. Subsequently, experiments on photovoltaic solar systems were carried out using two different PMAs, namely, perturb and observe (P&O) and incremental conductance (IC). Real-time data was collected, under different operating conditions, including thermal behavior using thermal imaging camera and dSPACE. Converters' thermal cycling was found to be approximately 3 • C higher with the IC algorithm. The steady state temperature was 52.7 • C, for the IC while it was 42.6 • C for P&O. Although IC algorithm offers more accurate power management tool, it causes more severe thermal stresses which, in this study, has led to approximately 1.4 times greater life consumption compared to P&O.