Monte Carlo-Based Reliability Estimation Methods for Power Devices in Power Electronics Systems

Novak, Mateja; Sangwongwanich, Ariya; Blaabjerg, Frede

doi:10.1109/ojpel.2021.3116070

Cited by 21 publications

(9 citation statements)

References 33 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…For higher fidelity modelling, the conclusions from [4], [24], [25] are that temperature and the cycling effects of temperature are pertinent causes of failure in PEC systems. Physics of Failure (PoF) approaches based on [5], [26], [27] detail such electrical and thermal modelling of the power converter and can be incorporated into Monte Carlo simulations to provide a more accurately defined lifetime estimate for the converter.…”

Section: Reliability Evaluation Of Power Electronic Convertersmentioning

confidence: 99%

System Wide Reliability Impact of Power Converters in More-Electric Aircraft Applications

Millar¹,

Fong²,

Alzola³

et al. 2023

SAE Technical Paper Series

View full text Add to dashboard Cite

<div class="section abstract"><div class="htmlview paragraph">The continued electrification of aircraft is required such that ambitious decarbonisation targets can be met. A significant challenge presented with this trend is the increased reliance on electrical systems to perform flight-critical operations in a manner that has not been seen in previous generations of aircraft. The power electronic converter is a key enabling technology in aircraft electrification. Its prevalence is such that the failure rate of flight critical-loads is closely linked with that of the associated power electronic converters. As such, there is a clear need to better understand the impact of improvements in both the reliability and failure estimation of novel power electronic converters at a systems level in future aerospace applications. Accordingly, this paper presents key highlights from literature on power converter research, summarising advances in reliability-enhancing features and more accurate Physics-of-Failure modelling methods. Simulation studies are then presented, which explore the impact of component-level advancements in power converters on the system-level failure rates and power system architecture configurations of flight-critical loads in more-electric aircraft applications.</div></div>

show abstract

Section: Reliability Evaluation Of Power Electronic Convertersmentioning

confidence: 99%

System Wide Reliability Impact of Power Converters in More-Electric Aircraft Applications

Millar¹,

Fong²,

Alzola³

et al. 2023

SAE Technical Paper Series

View full text Add to dashboard Cite

show abstract

“…In this paper, the rain flow counting method is used to calculate the IGBT thermal cycle number n i , the mean junction temperature T jm in each thermal cycle, the junction temperature fluctuation ΔT j and other IGBT thermal load information [35]. The IGBT failure cycles were calculated using the IGBT analytical life model shown in (21), which was derived from the accelerated power cycle test data.…”

Section: The Lcoe Of the Pv System Evaluation Processmentioning

confidence: 99%

“…The IGBT lifetime assessment process based on the mission profile is usually divided into three steps: first, the IGBT junction temperature is calculated based on the PV inverter mission profile, then thermal load statistics are performed based on the IGBT junction temperature information, and finally, the analytical lifetime model and the Miner criterion are used to estimate the IGBT lifetime [17][18][19]. At the same time, considering the randomness and difference of device and lifetime model parameters, references [20] and [21] used the Monte Carlo simulation method to randomly sample 10000 samples to obtain the lifetime distribution of power device IGBT. They also used the lifetime corresponding to 10% unreliability as IGBT lifetime evaluation.…”

Section: Introductionmentioning

confidence: 99%

BO‐XGBoost‐based voltage/var optimization for distribution network considering the LCOE of PV system

Zhang,

Gao,

Wang

et al. 2023

IET Renewable Power Gen

View full text Add to dashboard Cite

With large‐scale renewable energy connected to the distribution network, the traditional method to solve the distribution network voltage/var power optimization model can hardly meet the needs of online optimization of the distribution network. Therefore, a Bayesian optimization eXtreme Gradient Boosting (BO‐XGBoost) based optimization strategy for voltage/var of the distribution network considering the cost of the photovoltaic (PV) power is proposed to improve the solving speed of the optimization model. First, the voltage/var optimization model of the distribution network considering the LCOE of the PV system is established. The optimal reactive power output command data set of the PV generation in the distribution network is obtained by solving the established optimization model. The XGBoost model is used to mine the nonlinear mapping relationship between the state information of the distribution network and the optimal reactive power output of the PV system; then the Bayesian algorithm is used to complete the adaptive optimization of the hyperparameters of the XGBoost model. Finally, the PG&E 69 node power distribution system is used to verify the advantages of the proposed strategy in improving the performance of the XGBoost algorithm, increasing the solving speed of the voltage/var optimization model, and reducing the LCOE of PV generation.

show abstract

“…[117]. In this case, the Monte Carlo simulation (with a large number of simulations) will thus result in a distribution indicating the probability of each of the possible outcomes [118]. After that, the PDF is obtained using a distribution in Table . V. From the lifetime distribution of the component it is also possible to obtain the component unreliability function F(x), which is the CDF of the distribution (Fig.…”

Section: V) Parameter Estimation and Lifetime Distributionmentioning

confidence: 99%

An Overview of Lifetime Management of Power Electronic Converters

et al. 2022

View full text Add to dashboard Cite

An expected lifetime of converters is of great importance for optimal decision-making in the planning of modern Power Electronic (PE) systems. Hence, the lifetime management of power electronic systems has attracted a lot of attention in academia and industry. This paper is a guideline for managing the lifetime of power converters. Analyzing the different kinds of failures, failure modes and their corresponding mechanisms are investigated in the first section along with the failure data needed as input parameters of the assessment. In the second section, lifetime prediction in two aspects of component-level and system level is discussed and all the possible techniques to achieve them are investigated and compared. All the steps required to predict the lifetime in the component-level including electrothermal modeling, cycle counting, lifetime model, damage accumulation, parameter estimation, and lifetime distribution are described and then system level methods consisting of reliability block diagrams, fault-tree analysis, and Markov chains are examined and compared. The last section contains the roadmap of the lifetime extension including the reliable design and condition monitoring. INDEX TERMSReliability, Lifetime management, Lifetime analysis, Lifetime prediction, Lifetime extension, Empirical model, Physics of failure, failure mechanism FIGURE 1. Guideline of the reliability of power electronic systems.

show abstract

Monte Carlo-Based Reliability Estimation Methods for Power Devices in Power Electronics Systems

Cited by 21 publications

References 33 publications

System Wide Reliability Impact of Power Converters in More-Electric Aircraft Applications

System Wide Reliability Impact of Power Converters in More-Electric Aircraft Applications

BO‐XGBoost‐based voltage/var optimization for distribution network considering the LCOE of PV system

An Overview of Lifetime Management of Power Electronic Converters

Contact Info

Product

Resources

About