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

Boukezzi, Larbi; Bessissa, Lakhdar; Boubakeur, A.; Mahi, Djillali

doi:10.1007/s00521-016-2259-y

Cited by 21 publications

(12 citation statements)

References 36 publications

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“…This MF number was also considered adequate for the nature of the problem being addressed by the domain experts. Moreover, previous studies in the area corroborated this choice by showing that the typical number of membership functions for a linguistic variable is between three and seven [1,4,25], two and more than seven membership functions being a more rare case [2,11], and finally the evaluation results presented in Section 6.1 confirmed this choice of the number of membership functions.…”

Section: Inference Engine Parameters and Initial Db Definitionsupporting

confidence: 58%

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A design of fuzzy inference systems to predict tensile properties of as-cast alloy

Tan

Tarasov

Jarfors

et al. 2021

Int J Adv Manuf Technol

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In this study, a design of Mamdani type fuzzy inference systems is presented to predict tensile properties of as-cast alloy. To improve manufacturing of light weight cast components, understanding of mechanical properties of cast components under load is important. The ability of deterministic models to predict the performance of a cast component is limited due to the uncertainty and imprecision in casting data. Mamdani type fuzzy inference systems are introduced as a promising solution. Compared to other artificial intelligence approaches, Mandani type fuzzy models allow for a better result interpretation. The fuzzy inference systems were designed from data and experts’ knowledge and optimized using a genetic algorithm. The experts’ knowledge was used to set up the values for the inference engine and initial values for the database parameters. The rule base was automatically generated from the data which were collected from casting and tensile testing experiments. A genetic algorithm with real-valued coding was used to optimize the database parameters. The quality of the constructed systems was evaluated by comparing predicted and actual tensile properties, including yield strength, Y.modulus, and ultimate tensile strength, of as-case alloy from two series of casting and tensile testing experimental data. The obtained results showed that the quality of the systems has satisfactory accuracy and is similar to or better than several machine learning methods. The evaluation results also demonstrated good reliability and stability of the approach.

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Section: Inference Engine Parameters and Initial Db Definitionsupporting

confidence: 58%

“…The program stops when the number of generations reaches 100. The learning approach was implemented using SciPy, 2 which is an open source software for scientific computing in Python, and the FLSs were constructed using a fuzzy logic toolbox 3 for SciPy.…”

Section: Evaluation Of the Flsmentioning

confidence: 99%

A design of fuzzy inference systems to predict tensile properties of as-cast alloy

Tan

Tarasov

Jarfors

et al. 2021

Int J Adv Manuf Technol

View full text Add to dashboard Cite

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“…As such, it can be operated continuously up to 90°C and, under short circuit conditions, for a few seconds with conductor temperatures over 200°C. As a result of this combination of electrical, thermal and mechanical characteristics, XLPE has a long history of use in high voltage AC (HVAC) cable applications and, increasingly being refined for use in HVDC cable designs .…”

Section: The Emergence Of Thermoplastic Elastomer In Electrical Insulmentioning

confidence: 99%

A review of thermoplastic elastomeric nanocomposites for high voltage insulation applications

Ismail

Mariatti

2018

Polymer Engineering & Sci

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Thermoplastic elastomer (TPE) has gained acceptance as third generation of polymeric materials for high voltage (HV) insulators. The interesting features showed by TPEs were found to fulfill some particular requirements for HV insulations, at least for the distribution class applications, especially at light contamination environments. Introduction of nanomaterials which widely reported to give significant improvements in the electrical, mechanical, thermal and other properties of polymer has attracted attention to the synergistic properties of combining the two complementary technologies of thermoplastic elastomers and nanocomposites. In this paper, research works on various types of TPEs having promising performance as HV insulators are thoroughly reviewed. The emergence of nanocomposites in the electrical insulation field and the important properties consideration for HV insulators as well as the processing techniques of TPE nanocomposites are also discussed. Finally, some of the past and recent developments of TPE nanocomposites focusing on the electrical insulation properties are highlighted in this article. POLYM. ENG. SCI., 58:E36–E63, 2018. © 2018 Society of Plastics Engineers

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“…Space charge is liable to accumulate in polyethylene, which can distort the local electric field and affect its service [38]. The space charge measurement was carried out with the Chongqing University pulsed electro-acoustic (PEA) system.…”

Section: Characterizationmentioning

confidence: 99%

Effect of Doping Microcapsules on Typical Electrical Performances of Self-Healing Polyethylene Insulating Composite

Wang

Zhang

et al. 2019

Applied Sciences

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Polyethylene cables, as important transmission equipment of modern power grid, would inevitably be slightly damaged, which seriously threatens the safety of the power supply. This paper has pioneered the preparation and typical performances of a self-healing polyethylene insulating composite. The self-healing performance to structural damage was verified by tests of electrical and mechanical damage. The effect mechanism of doping microcapsules on the electrical performance of polyethylene was emphatically analyzed. The results show that in appropriate conditions (such as 60 • C/30 min), the composite can not only repair the electrical tree and scratches, but also restore the insulation strength of damaged area. The effect of doping microcapsules on the electrical performances of polyethylene, such as breakdown strength, volumetric resistivity, dielectric properties, and space charge characteristics, are mainly related to impurity and the interface of microcapsule. Polarization and ionization of impurities can reduce the electrical performance of polyethylene. The interface not only improves the microstructure of polyethylene (such as how the heterogeneous nucleation effect increases the number of crystal regions, and the anchoring effect enhances the stability of amorphous regions), but also increases the charge traps. Moreover, the microstructure and charge trap can affect the characteristics of carrier transport, material polarization, and space charge accumulation, thus improving the electrical performance of polyethylene. In addition, the important electrical performance of the composite can meet the basic application requirements of polyethylene insulating material, which has good application prospects.

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

Cited by 21 publications

References 36 publications

A design of fuzzy inference systems to predict tensile properties of as-cast alloy

A design of fuzzy inference systems to predict tensile properties of as-cast alloy

A review of thermoplastic elastomeric nanocomposites for high voltage insulation applications

Effect of Doping Microcapsules on Typical Electrical Performances of Self-Healing Polyethylene Insulating Composite

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