Prediction of mechanical properties of XLPE cable insulation under thermal aging: neural network approach

Boukezzi, Larbi; Boubakeur, A.

doi:10.1109/tdei.2013.6678861

Cited by 32 publications

(19 citation statements)

References 13 publications

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“…In the following, we will only present the outline of the used RBF neural network. Further detail can be found in [34,35]. The RBF neural network used here consists of three layers, namely the input layer, the hidden layer and the output layer (Fig.…”

Section: Artificial Neural Network (Ann) Modelmentioning

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: Artificial Neural Network (Ann) Modelmentioning

confidence: 99%

“…3, and more details are given in our previous work [34,37]. The proposed modelling procedure is carried out with the help of 54 sets of experimental input-output patterns which represent the different values of different properties according to the aging time and under the two aging temperatures.…”

Section: Artificial Neural Network (Ann) Modelmentioning

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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“…The input layer and the output layer are the basic structures forming the neural network; the hidden layer is dispensable. The basic structure is shown in Figure 4 [14].…”

Section: Neural Networkmentioning

confidence: 99%

Chaotic Extension Neural Network-Based Fault Diagnosis Method for Solar Photovoltaic Systems

Yau

2014

Mathematical Problems in Engineering

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At present, the solar photovoltaic system is extensively used. However, once a fault occurs, it is inspected manually, which is not economical. In order to remedy the defect of unavailable fault diagnosis at any irradiance and temperature in the literature with chaos synchronization based intelligent fault diagnosis for photovoltaic systems proposed by Hsieh et al., this study proposed a chaotic extension fault diagnosis method combined with error back propagation neural network to overcome this problem. It used the nn toolbox of matlab 2010 for simulation and comparison, measured current irradiance and temperature, and used the maximum power point tracking (MPPT) for chaotic extraction of eigenvalue. The range of extension field was determined by neural network. Finally, the voltage eigenvalue obtained from current temperature and irradiance was used for the fault diagnosis. Comparing the diagnostic rates with the results by Hsieh et al., this scheme can obtain better diagnostic rates when the irradiances or the temperatures are changed.

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“…They analysed the life expectancy model of XLPE cable material property that is using thermal and mechanical applications, the research informing that the probability of melting range was 50% from 1 to 5 years of cable operation period for more than 40° C and for 10-years operation period, the probability of melting range is 89% [12]. Similar study was carried out in XLPE cable insulation that is using electrical and mechanical properties analysis [13].The experimental study application of thermal aging of XLPE and PVC cables are applying voltage and current on the cable to measure the current through the insulation and identify the total harmonic distortion. The author has applied hot air to the cable insulation for temperature simulation [14].…”

Section: Introductionmentioning

confidence: 99%

Modeling and Analysis of the Remaining Useful Life of MV XLPE Cable: Case study of Oman Oil and Gas Power Grid

Qatan

Farrag

Alkali

et al. 2018

2018 53rd International Universities Power Engineering Conference (UPEC)

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Cables are one of the main assets in the power grid particularly for distribution network where the loads are widely spaced, this is a vital issue in oil and gas power grid. Monitoring cables on-line is an impossible task due to the cost and the excessive number of distributed cables. In most of oil and gas production countries the ambient temperature hits 56 degree Celsius that adds to the failures of power cables and shorten the cables lifecycle and the hour to hour capacity. This paper presents the results of estimating the maximum operating temperature of the cable during different operating load and under different ambient temperature, it is anticipating its impacts on the cables lifecycle. This is compared to the cable manufacturer design specification associated with the normal operating temperature. The results identify the factors contribute significantly to the degradation of the physical insulation property of the cables. In this paper the response time in the thermal transient model is used to identify the remaining useful life (RUL) of the cable. The model is used as a diagnostic procedure to detect the degree of aging, the results show that RUL of underground cables is7.5 years and for overhead cables is13.3 years under ambient temperature of 47°C and maximum load current.

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Prediction of mechanical properties of XLPE cable insulation under thermal aging: neural network approach

Cited by 32 publications

References 13 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

Chaotic Extension Neural Network-Based Fault Diagnosis Method for Solar Photovoltaic Systems

Modeling and Analysis of the Remaining Useful Life of MV XLPE Cable: Case study of Oman Oil and Gas Power Grid

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