Bayesian reliability estimation based on a weibull stress-strength model for aged power system components subjected to voltage surges

Chiodo, E.; Mazzanti, G.

doi:10.1109/tdei.2006.1593413

Cited by 38 publications

(40 citation statements)

References 19 publications

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“…Such an analysis, performed according to the approaches in [18,19], allows to obtain also in this case very encouraging outcomes. A detailed dissertation is reported below.…”

Section: Numerical Applicationsmentioning

confidence: 92%

“…Finally, since it is well known that the choice of the prior distribution is often based upon very little data, and is -to a large extent -arbitrary [18][19][20][21], the following section discusses the results obtained carrying out a proper 'robustness analysis'. Such an analysis, performed according to the approaches in [18,19], allows to obtain also in this case very encouraging outcomes.…”

Section: Numerical Applicationsmentioning

confidence: 99%

“…In the extreme case that none data (or almost none data) is available, the only way to assign values to the availability parameters is by similarity to analogous systems, 'prior guesses', experts' judgments and so on: all methods will obviously yield estimates possessing an inherently high 'variance', and certainly cannot be considered as deterministic values, although they are treated as such in most of the relevant literature. All the aspects just mentioned, well highlighted in [15][16][17], can be properly managed via a Bayesian approach [14,[17][18][19][20][21]. Under the hypothesis that the Exponential model holds for both PVI reliability (failure times) and maintainability (repair times), the uncertainty is basically condensed in the hazard rate (HR) and repair rate (RR) parameters.…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Availability analysis of photovoltaic inverters in presence of uncertain data via Bayesian approach

Chiodo¹,

Lauria²,

Pisani³

2015

IET Generation, Transmission & Distribution

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The paper presents an advanced Bayesian technique to carry out availability analyses of photovoltaic inverters in presence of uncertain reliability data. In order to proper take into account the above uncertainty, photovoltaic inverter hazard rate and repair rate are considered as random variables characterized by proper Log-normal and/or Gamma distributions. These assumptions are adequately motivated by well known and established physical and mathematical considerations. In this manner the photovoltaic availability uncertainty can be expressed as a function of the component uncertain data. Afterwards, thanks to the tailored Bayesian technique we demonstrate how, even in presence of scarce data, an efficient updating of the system performance can be accomplished during the photovoltaic inverter operating life. The methodology is really useful in view of an optimal allocation of the components’ reliabilities at the design stage and in order to establish the actual expected payback of the whole photovoltaic system. Extensive numerical simulations confirm the effectiveness and the validity of the developed Bayesian technique by also verifying its inherent robustness and efficiency through a rigorous analysis

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“…Such an analysis, performed according to the approaches in [18,19], allows to obtain also in this case very encouraging outcomes. A detailed dissertation is reported below.…”

Section: Numerical Applicationsmentioning

confidence: 92%

Section: Numerical Applicationsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Availability analysis of photovoltaic inverters in presence of uncertain data via Bayesian approach

Chiodo¹,

Lauria²,

Pisani³

2015

IET Generation, Transmission & Distribution

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“…For more details, see Chiodo et al [4], Chiodo and Mazzanti [5] and Eryılmaz [10]. Clearly, using (3.4) and (3.5) for t = 0.1, 0.2, · · · , 2 and selected values of the parameters θ 1 and θ 2 , we can obtain D (t) KL(l) and R l (t) values presented in Table 1.…”

Section: Gamma Distributional Examplementioning

confidence: 96%

Dynamic reliability evaluation for a multi-state component under stress-strength model

Çalik¹

2017

J. Nonlinear Sci. Appl.

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For many technical systems, stress-strength models are of special importance. Stress-strength models can be described as an assessment of the reliability of the component in terms of X and Y random variables where X is the random "stress" experienced by the component and Y is the random "strength" of the component available to overcome the stress. The reliability of the component is the probability that component is strong enough to overcome the stress applied on it. Traditionally, both the strength of the component and the applied stress are considered to be both time-independent random variables. But in most of real life systems, the status of a stress and strength random variables clearly change dynamically with time. Also, in many important systems, it is very necessary to estimate the reliability of the component without waiting to observe the component failure. In this paper we study multi-state component where component is subjected to two stresses. In particular, inspired by the idea of Kullback-Leibler divergence, we aim to propose a new method to compute the dynamic reliability of the component under stress-strength model. The advantage of the proposed method is that Kullback-Leibler divergence is equal to zero when the component strength is equal to applied stress. In addition, the formed function can include both stresses when two stresses exist at the same time. Also, the proposed method provides a simple way and good alternative to compute the reliability of the component in case of at least one of the stress or strengths quantities depend on time.

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“…The assumption of Gamma pdf for the Exponential parameter Z is widely accepted in Bayesian literature [21], since it is very flexible, allowing to approximate many empirical pdfs, and is a "conjugate" pdf (i.e., the posterior pdf, with updated parameter values, is again Gamma). Furthermore, it appears to be robust with respect to the estimation of the reliability [21], [22]. This implies that a "Gamma Exponential" (GE) distribution -below illustrated -is, under proper assumptions, the valid one for each of the parameters N 1 , N 2 , N 3 .…”

Section: Bayesian Estimation Of the Insulation Lifetime Accordingmentioning

confidence: 99%

Bayes parametric estimation of insulation reliability under distorted voltage

Chiodo

Mazzanti

2014

2014 International Symposium on Power Electronics, Electrical Drives, Automation and Motion

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Estimating the useful life of power system components is of considerable importance both in planning, to properly assess the costs, and in the management of such systems, to effectively schedule maintenance programs. The paper discusses the reliability of the insulation of power system components on the basis of well-known life models of such devices, with the aim of parametric statistical estimation of such models. With particular reference to insulated cables for traction systems under distorted regime, an experimentally-based and commonly adopted mathematical relationship between the lifetime of a given insulation affected by non-sinusoidal voltage and the levels of the harmonic voltages applied to such insulation is adopted, that is of the kind of the well-known Inverse Power Model (IPM). A Bayesian inference method for the estimation of the above model is illustrated, when the Gamma Exponential prior distribution -a new model here proposed -holds for the basic parameters of the above Inverse Power Model. The performance of these estimators are empirically analyzed through extensive numerical simulations under a wide range of parameter values. All the results show the feasibility and efficiency of such Bayes estimation, especially for very small sample sizes, as requested for the above applications.

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Bayesian reliability estimation based on a weibull stress-strength model for aged power system components subjected to voltage surges

Cited by 38 publications

References 19 publications

Availability analysis of photovoltaic inverters in presence of uncertain data via Bayesian approach

Availability analysis of photovoltaic inverters in presence of uncertain data via Bayesian approach

Dynamic reliability evaluation for a multi-state component under stress-strength model

Bayes parametric estimation of insulation reliability under distorted voltage

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