Artificial neural network optimisation methodology for the estimation of the critical flashover voltage on insulators

Asimakopoulou, G.E.; Kontargyri, Vassiliki T.; Tsekouras, George E.; Asimakopoulou, Fani E.; Gonos, Ioannis F.; Stathopulos, I.A.

doi:10.1049/iet-smt:20080009

Cited by 28 publications

(23 citation statements)

References 20 publications

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“…The functionality of 12 different training algorithms, which are used in this work, is synopsized in Table 10 . A short description of all training algorithms is presented in Table 10 [ 25 ] while more analytical representations are shown in Table 10 . [ 13 14 15 16 17 18 19 20 21 22 23 24 ] The basic steps of the back-propagation algorithm have been described in several textbooks.…”

Section: Resultsmentioning

confidence: 99%

Breathomics for gastric cancer classification using back-propagation neural network

Daniel¹,

Thangavel

2016

J Med Signals Sens

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Breathomics is the metabolomics study of exhaled air. It is a powerful emerging metabolomics research field that mainly focuses on health-related volatile organic compounds (VOCs). Since the quantity of these compounds varies with health status, breathomics assures to deliver noninvasive diagnostic tools. Thus, the main aim of breathomics is to discover patterns of VOCs related to abnormal metabolic processes occurring in the human body. Classification systems, however, are not designed for cost-sensitive classification domains. Therefore, they do not work on the gastric carcinoma (GC) domain where the benefit of correct classification of early stages is more than that of later stages, and also the cost of wrong classification is different for all pairs of predicted and actual classes. The aim of this work is to demonstrate the basic principles for the breathomics to classify the GC, for that the determination of VOCs such as acetone, carbon disulfide, 2-propanol, ethyl alcohol, and ethyl acetate in exhaled air and stomach tissue emission for the detection of GC has been analyzed. The breath of 49 GC and 30 gastric ulcer patients were collected for the study to distinguish the normal, suspected, and positive cases using back-propagation neural network (BPN) and produced the accuracy of 93%, sensitivity of 94.38%, and specificity of 89.93%. This study carries out the comparative study of the result obtained by the single- and multi-layer cascade-forward and feed-forward BPN with different activation functions. From this study, the multilayer cascade-forward outperforms the classification of GC from normal and benign cases.

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Section: Resultsmentioning

confidence: 99%

Breathomics for gastric cancer classification using back-propagation neural network

Daniel¹,

Thangavel

2016

J Med Signals Sens

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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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“…So far, researchers such as Salam et al [18], Amaral et al [19] and Gouda et al [20] have successfully used ANNs attempting to model the correlation among ground resistance, soil resistivity and length of buried electrodes in soil, injection current frequency and peak current. This paper is an extension to the research presented in [21–24], in an effort to consider concisely all the previous work on ANN modelling and experiment on a modified architecture of a greater number of input and output variables, tested under faster training algorithms.…”

Section: Introductionmentioning

confidence: 99%

Artificial neural network methodology for the estimation of ground enhancing compounds resistance

Androvitsaneas

Gonos

Stathopulos

2014

IET Science, Measurement & Technology

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The work presented in this study aims to develop a methodological approach for estimating the ground resistance of several grounding systems, embedded in various ground enhancing compounds, using artificial neural networks (ANNs). The ANN training is based on field measurements that have been performed in Greece during last years. The methodology uses as input variables measurements of soil resistivity within various depths and of rainfall height during some periods of time, like last week and last month and estimates the ground resistance value of the tested rods, based on an ANN. This work comprises two scenarios in which, several ANN training algorithms are applied and an optimisation process is performed regarding the values of parameters, such as the number of neurons, the activation functions combination and so on. Each training algorithm is compared to the others, based on the coefficient of determination between the experimental and estimated values for the test set and the algorithm with the best results is highlighted for the estimation of ground resistance value, formed by the ground enhancing compounds under various weather conditions.

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Artificial neural network optimisation methodology for the estimation of the critical flashover voltage on insulators

Cited by 28 publications

References 20 publications

Breathomics for gastric cancer classification using back-propagation neural network

Breathomics for gastric cancer classification using back-propagation neural network

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

Artificial neural network methodology for the estimation of ground enhancing compounds resistance

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