Investigations of a mechanically failed cable insulation used in indoor conditions

Ilie, S.; Setnescu, Radu; Lungulescu, Eduard-Marius; Marinescu, Virgil; Ilie, Daniela Elena; Setnescu, Tanţa; Mares, G. J.

doi:10.1016/j.polymertesting.2010.11.016

Cited by 21 publications

(20 citation statements)

References 21 publications

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“…In such cable, the electrical insulation is assured by polymeric materials [1]. Compared to some types of polymers such as polyvinyl chloride (PVC), ethylene-propylene copolymer (EPR) and ethylene-acetate copolymer (EVA) which are of common use for such purpose, the polyethylene (PE) in its crosslinked form (XLPE) is highly recommended [2].…”

Section: Introductionmentioning

confidence: 99%

“…Among others, one can quote: presence of voids and impurities in the material, incorrect handling during installation, inappropriate mechanical and electrical use and aging of polymeric insulation under service conditions. This latter presents the most important cause of the cable failure [1]. Under service conditions, the cable is permanently subjected to electrical, thermal, mechanical and environmental stresses [5].…”

Section: Introductionmentioning

confidence: 99%

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Neural networks and fuzzy logic approaches to predict mechanical properties of XLPE insulation cables under thermal aging

Boukezzi

Bessissa

Boubakeur

et al. 2016

Neural Comput & Applic

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The widespread use of cross-linked polyethylene (XLPE) as insulation in the manufacturing of medium-and high-voltage cables may be attributed to its outstanding mechanical and electrical properties. However, it is well known that degradation under service conditions is the major problem in the use of XLPE as cable insulation. Laboratory investigations of the insulations aging are time-consuming and cost-effective. To avoid such costs, we have developed two models which are based on artificial neural networks (ANNs) and fuzzy logic (FL) to predict the insulation properties under thermal aging. The proposed ANN is a supervised one based on radial basis function Gaussian and trained by random optimization method algorithm. The FL model is based on the use of fuzzy inference system. Both models are used to predict the mechanical properties of thermally aged XLPE. The obtained results are evaluated and compared to the experimental data in depth by using many statistical parameters. It is concluded that both models give practically the same prediction quality. In particular, they have ability to reproduce the nonlinear behavior of the insulation properties under thermal aging within acceptable error. Furthermore, our ANN and FL models can be used in the generalization phase where the prediction of the future state (not reached experimentally) of the insulation is made possible. Additionally, costs and time could be reduced.

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Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Neural networks and fuzzy logic approaches to predict mechanical properties of XLPE insulation cables under thermal aging

Boukezzi

Bessissa

Boubakeur

et al. 2016

Neural Comput & Applic

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“…Differential scanning calorimetry DSC measurements were taken by Setaram DSC 131 Evo instrument (Setaram Instrumentation, France) following a procedure earlier described [33]. Samples with a thickness of around 0.5 mm were sliced from the zone of interest of PE insulation.…”

Section: Chemiluminescencementioning

confidence: 99%

“…The hydroperoxide content as it is described by DH 150 parameter (i.e., the heat evolved in the temperature range 120-190°C under nitrogen atmosphere) [33,[41][42][43] increased by comparison with the untreated sample (Tables 4 and 5) in a similar way to the initial CL signal (I 0 ). The increase in DH 150 values is, however, less clear than in CL which is more sensitive in detection of both sample history and early oxidation stages [32].…”

Section: Differential Scanning Calorimetrymentioning

confidence: 99%

“…The secondary hypothesis of this work is the capability of measurement procedures, chemiluminescence (CL) and differential scanning calorimetry (DSC) to characterize the catalytic effect of diffused Cu at elevated temperatures [22,26,27]. Taking into account the univocal relationship between the oxidation induction time (OIT) and material stability [28,33], it is also foreseen that the samples containing Cu will present lower OIT values as compared to copper-free ones. The presence of copper within the insulating material was clearly confirmed and quantified by different elemental analysis methods.…”

Section: Introductionmentioning

confidence: 99%

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Copper diffusion in cable-insulating materials by chemiluminescence and DSC techniques

Jipa

Setnescu

Zaharescu³

et al. 2015

J Therm Anal Calorim

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Chemiluminescence (CL) and differential scanning calorimetry (DSC) were applied for proving the copper ion migration processes occurring within the lowdensity polyethylene (LDPE) electrical insulator as a consequence of intimate contact. Thin LDPE slides of 0.5 mm thick were subjected to short-time thermal stress at temperatures between 80 and 120°C. Both CL and DSC kinetic parameters revealed the acceleration of posttreatment oxidation at elevated temperatures (170-200°C) in copper-contacting polymer as in respect to the cases when Cu was absent. It was revealed that the oxidation rate measured by CL was more than two times higher for the samples heated in the presence of Cu than in its absence, while the oxidation induction time from DSC was considerably lowered for the same samples. Elemental analysis data provided by atomic absorption spectroscopy and particle-induced X-ray emission techniques as well as SEM investigations confirmed the presence of copper within the samples subjected to thermal treatment when the polymer insulation was in contact with the metallic conductor. Chemiluminescence procedure has a remarkable sensitivity to detect the effect of copper in the polymer matrix even at low concentrations.

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Influence of the Insulation Defects Size on the Value of the Wire Capacitance

Vavilova

Yurchenko

Li³

2021

Progress in Material Science and Engineering

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Investigations of a mechanically failed cable insulation used in indoor conditions

Cited by 21 publications

References 21 publications

Neural networks and fuzzy logic approaches to predict mechanical properties of XLPE insulation cables under thermal aging

Neural networks and fuzzy logic approaches to predict mechanical properties of XLPE insulation cables under thermal aging

Copper diffusion in cable-insulating materials by chemiluminescence and DSC techniques

Influence of the Insulation Defects Size on the Value of the Wire Capacitance

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