Prediction of leakage current of non-ceramic insulators in early aging period

El-Hag, Ayman H.; Jahromi, Ali Naderian; Sanaye‐Pasand, M.

doi:10.1016/j.epsr.2008.02.010

Cited by 24 publications

(13 citation statements)

References 16 publications

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“…The general scheme of the developed FIS is presented in Fig. 4, and further details and description of its different parts are available in [30][31][32]. The chosen input/output membership functions are presented in Fig.…”

Section: Fuzzy Logic Modelmentioning

confidence: 99%

“…In electrical engineering, especially in high-voltage field, a lot of applications of ANN can be found [26][27][28][29][30]. In partial discharge pattern recognition in high-voltage power apparatus, Chen et al [26] investigated a novel extension neural network method.…”

Section: Introductionmentioning

confidence: 99%

“…Teguar et al [28] and Gençoglu and Cebeci [29] investigated two types of neural networks (RBFG and MLP) to predict the flashover of polluted high-voltage insulators. To predict the leakage current under aging conditions of nonceramic insulators, El-Hag et al [30] use the RBFG neural network trained with two-algorithm (BP and ROM) method. However, a small amount of papers dealing with applications of fuzzy logic in high-voltage engineering field can be found in the literature [31][32][33].…”

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: Fuzzy Logic Modelmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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 critical flashover voltage for outdoor insulators was predicted using Artificial Neural Networks (ANN) in [37,38]. Similar models have been developed to predict the critical flashover voltage and leakage current of outdoor insulators [39][40][41][42][43][44]. However, the training dataset used in these studies consisted of the insulator diameter, height, ESDD, creepage distance and form factor.…”

Section: Introductionmentioning

confidence: 99%

Forecasting Flashover Parameters of Polymeric Insulators under Contaminated Conditions Using the Machine Learning Technique

et al. 2020

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There is a vital need to understand the flashover process of polymeric insulators for safe and reliable power system operation. This paper provides a rigorous investigation of forecasting the flashover parameters of High Temperature Vulcanized (HTV) silicone rubber based on environmental and polluted conditions using machine learning. The modified solid layer method based on the IEC 60507 standard was utilised to prepare samples in the laboratory. The effect of various factors including Equivalent Salt Deposit Density (ESDD), Non-soluble Salt Deposit Density (NSDD), relative humidity and ambient temperature, were investigated on arc inception voltage, flashover voltage and surface resistance. The experimental results were utilised to engineer a machine learning based intelligent system for predicting the aforementioned flashover parameters. A number of machine learning algorithms such as Artificial Neural Network (ANN), Polynomial Support Vector Machine (PSVM), Gaussian SVM (GSVM), Decision Tree (DT) and Least-Squares Boosting Ensemble (LSBE) were explored in forecasting of the flashover parameters. The prediction accuracy of the model was validated with a number of error cost functions, such as Root Mean Squared Error (RMSE), Normalized RMSE (NRMSE), Mean Absolute Percentage Error (MAPE) and R. For improved prediction accuracy, bootstrapping was used to increase the sample space. The proposed PSVM technique demonstrated the best performance accuracy compared to other machine learning models. The presented machine learning model provides promising results and demonstrates highly accurate prediction of the arc inception voltage, flashover voltage and surface resistance of silicone rubber insulators in various contaminated and humid conditions.

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“…Another approach [10] is performed by the neural network technique for predicting the leakage currents on insulators silicone rubber in salt spray test.…”

Section: Introductionmentioning

confidence: 99%

Study of dielectric properties of composite insulators for use in medium voltage overhead lines

Mahdhi

Askri

Raouadi

et al. 2013

Journal of Electrostatics

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Prediction of leakage current of non-ceramic insulators in early aging period

Cited by 24 publications

References 16 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

Forecasting Flashover Parameters of Polymeric Insulators under Contaminated Conditions Using the Machine Learning Technique

Study of dielectric properties of composite insulators for use in medium voltage overhead lines

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